In AdrianAntico/ModelingTools: AutoQuant
```{css, echo=FALSE}
@import url('https://fonts.googleapis.com/css2?family=Yusei+Magic&display=swap');
- {
box-sizing: border-box;
}
body {
padding: 0;
margin: 0;
font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 16px;
line-height: 1.5;
color: #000000;
background: linear-gradient(180deg, #787989, #b7b8cf);
}
a {
color: #b1c5d8;
text-decoration: none;
}
a:hover {
text-decoration: underline;
}
.page-header {
color: #fff;
text-align: center;
background-color: #000121;
background-image: linear-gradient(120deg,#000121,#696b76);
padding: 1.5rem 2rem;
}
.page-header :last-child {
margin-bottom: 0.5rem;
}
@media screen and (max-width: 42em) {
.page-header {
padding: 1rem 1rem;
}
}
.project-name {
margin-top: 0;
margin-bottom: 0.1rem;
font-size: 2rem;
}
@media screen and (max-width: 42em) {
.project-name {
font-size: 1.75rem;
}
}
.project-tagline {
margin-bottom: 2rem;
font-weight: normal;
opacity: 0.7;
font-size: 1.5rem;
}
@media screen and (max-width: 42em) {
.project-tagline {
font-size: 1.2rem;
}
}
.project-author, .project-date {
font-weight: normal;
opacity: 0.7;
font-size: 1.2rem;
}
@media screen and (max-width: 42em) {
.project-author, .project-date {
font-size: 1rem;
}
}
.main-content, .toc {
max-width: 64rem;
padding: 2rem 4rem;
margin: 0 auto;
font-size: 1.1rem;
}
.toc {
padding-bottom: 0;
}
.toc .toc-box {
padding: 55px;
background: linear-gradient(90deg, #000121, #696b76);
border: solid 1px #000000;
border-radius: 70px;
color: white;
box-shadow: 8px 5px 10px 0px #000000;
}
.toc .toc-box .toc-title {
margin: 0 0 0.5rem;
text-align: center;
}
.toc .toc-box > ul {
margin: 0;
padding-left: 1.5rem;
}
@media screen and (min-width: 42em) and (max-width: 64em) {
.toc {
padding: 2rem 2rem 0;
}
}
@media screen and (max-width: 42em) {
.toc {
padding: 2rem 1rem 0;
font-size: 1rem;
}
}
.main-content :first-child {
margin-top: 0;
}
@media screen and (min-width: 42em) and (max-width: 64em) {
.main-content {
padding: 2rem;
}
}
@media screen and (max-width: 42em) {
.main-content {
padding: 2rem 1rem;
font-size: 1rem;
}
}
.main-content img {
max-width: 100%;
}
.main-content h1 {
margin-top: 2rem;
margin-bottom: 1rem;
font-weight: bold;
color: #070e56;
}
.main-content h2 {
margin-top: 2rem;
margin-bottom: 1rem;
font-weight: normal;
color: #121e94;
}
.main-content h3 {
margin-top: 2rem;
margin-bottom: 1rem;
font-weight: normal;
color: #3745d1;
}
.main-content h4 {
margin-top: 2rem;
margin-bottom: 1rem;
font-weight: normal;
color: #374995;
}
.main-content h5 {
margin-top: 2rem;
margin-bottom: 1rem;
font-weight: normal;
color: #000000;
}
.main-content h6 {
margin-top: 2rem;
margin-bottom: 1rem;
font-weight: normal;
color: #000000;
}
.main-content p {
margin-bottom: 1em;
}
.main-content code {
padding: 2px 4px;
font-family: Consolas, "Liberation Mono", Menlo, Courier, monospace;
color: #2c2c2d;
background-color: #f3f6fa;
border-radius: 0.3rem;
}
.main-content pre {
padding: 0.8rem;
margin-top: 0;
margin-bottom: 1rem;
font: 1rem Consolas, "Liberation Mono", Menlo, Courier, monospace;
color: #2c2c2d;
word-wrap: normal;
background-color: #e9edf9c9;
border: solid 1px #001a35;
border-radius: 0.3rem;
line-height: 1.45;
overflow: auto;
}
@media screen and (max-width: 42em) {
.main-content pre {
font-size: 0.9rem;
}
}
.main-content pre > code {
padding: 0;
margin: 0;
color: #2c2c2d;
word-break: normal;
white-space: pre;
background: transparent;
border: 0;
}
@media screen and (max-width: 42em) {
.main-content pre > code {
font-size: 0.9rem;
}
}
.main-content pre code,
.main-content pre tt {
display: inline;
max-width: initial;
padding: 0;
margin: 0;
overflow: initial;
line-height: inherit;
word-wrap: normal;
background-color: transparent;
border: 0;
}
.main-content pre code:before, .main-content pre code:after,
.main-content pre tt:before,
.main-content pre tt:after {
content: normal;
}
.main-content ul,
.main-content ol {
margin-top: 0;
}
.main-content blockquote {
padding: 0 1rem;
margin-left: 0;
color: #2c2c2d;
border-left: 0.3rem solid #dce6f0;
font-size: 1.2rem;
}
.main-content blockquote > :first-child {
margin-top: 0;
}
.main-content blockquote > :last-child {
margin-bottom: 0;
}
@media screen and (max-width: 42em) {
.main-content blockquote {
font-size: 1.1rem;
}
}
.main-content table {
width: 100%;
overflow: auto;
word-break: normal;
word-break: keep-all;
-webkit-overflow-scrolling: touch;
border-collapse: collapse;
border-spacing: 0;
margin: 1rem 0;
}
.main-content table th {
font-weight: bold;
background-color: #159957;
color: #fff;
}
.main-content table th,
.main-content table td {
padding: 0.5rem 1rem;
border-bottom: 1px solid #e9ebec;
text-align: left;
}
.main-content table tr:nth-child(odd) {
background-color: #f2f2f2;
}
.main-content dl {
padding: 0;
}
.main-content dl dt {
padding: 0;
margin-top: 1rem;
font-size: 1rem;
font-weight: bold;
}
.main-content dl dd {
padding: 0;
margin-bottom: 1rem;
}
.main-content hr {
height: 2px;
padding: 0;
margin: 1rem 0;
background-color: #eff0f1;
border: 0;
}
```r
if(!is.null(ModelObject)) {
# Model MetaData ----
## Model_MetaData_Parameters ----
ArgsList <- ModelObject[['ArgsList']]
## Model_MetaData_GridMetrics ----
GridMetrics <- ModelObject[['GridMetrics']]
}
if(!is.null(ModelObject)) {
# DataSets
TestData <- ModelObject[['TestData']]
ValidationData <- ModelObject[['ValidationData']]
TrainData <- ModelObject[['TrainData']]
# Meta info
TargetColumnName <- ModelObject[['ArgsList']][['TargetColumnName']]
PredictionColumnName <- PredictionColumnName
if(is.null(PredictionColumnName)) {
PredictionColumnName <- ModelObject[['ColNames']][[1L]]
}
}
if(!is.null(ModelObject)) {
# Evaluation Metrics ----
## Model_Evaluation_Metrics (catboost check, h2o check) ----
Test_EvalMetrics <- ModelObject[['EvaluationMetrics']][['TestData']]
Train_EvalMetrics <- ModelObject[['EvaluationMetrics']][['TrainData']]
## Model_VarImportanceTable ----
if(tolower(Algo) == 'catboost') {
# Store data
Test_Importance <- ModelObject[['VariableImportance']][['Test_Importance']]
Validation_Importance <- ModelObject[['VariableImportance']][['Validation_Importance']]
Train_Importance <- ModelObject[['VariableImportance']][['Train_Importance']]
# Update Colnames
if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = 'Importance', new = 'Test_Importance', skip_absent = TRUE)
if(!is.null(Validation_Importance)) data.table::setnames(Validation_Importance, old = 'Importance', new = 'Validation_Importance', skip_absent = TRUE)
if(!is.null(Train_Importance)) data.table::setnames(Train_Importance, old = 'Importance', new = 'Train_Importance', skip_absent = TRUE)
# CatBoost only
if(is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) {
All_Importance <- NULL
} else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) {
All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE)
All_Importance <- merge(All_Importance, Train_Importance, by = 'Variable', all = TRUE)
} else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) {
All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE)
} else if(!is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) {
All_Importance <- merge(Test_Importance, Train_Importance, by = 'Variable', all = TRUE)
} else if(is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) {
All_Importance <- merge(Validation_Importance, Train_Importance, by = 'Variable', all = TRUE)
} else if(is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) {
All_Importance <- Train_Importance
} else if(is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) {
All_Importance <- Validation_Importance
} else if(!is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) {
All_Importance <- Test_Importance
} else {
All_Importance <- NULL
}
} else {
# Store data (xgb check, )
Test_Importance <- ModelObject[['VariableImportance']]
Validation_Importance <- NULL
Train_Importance <- NULL
# Update Colnames
if(Algo %in% c("xgboost","lightgbm")) {
if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = names(Test_Importance)[2], new = 'Test_Importance', skip_absent = TRUE)
} else {
# h2o col 3 is scaled importance which is preferred
if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = names(Test_Importance)[3], new = 'Test_Importance', skip_absent = TRUE)
}
# Non CatBoost only
if(!is.null(Test_Importance)) {
All_Importance <- Test_Importance
} else {
All_Importance <- NULL
}
}
## Model_IntImportanceTable ----
if(tolower(Algo) == 'catboost') {
Test_Interaction <- ModelObject[['InteractionImportance']][['Test_Interaction']]
Validation_Interaction <- ModelObject[['InteractionImportance']][['Validation_Interaction']]
Train_Interaction <- ModelObject[['InteractionImportance']][['Train_Interaction']]
# Update Colnames
if(!is.null(Test_Interaction)) data.table::setnames(Test_Interaction, old = 'score', new = 'Test_Importance', skip_absent = TRUE)
if(!is.null(Validation_Interaction)) data.table::setnames(Validation_Interaction, old = 'score', new = 'Validation_Importance', skip_absent = TRUE)
if(!is.null(Train_Interaction)) data.table::setnames(Train_Interaction, old = 'score', new = 'Train_Importance', skip_absent = TRUE)
# CatBoost only
if(is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) {
All_Interaction <- NULL
} else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) {
All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE)
All_Interaction <- merge(All_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE)
data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1)
} else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) {
All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE)
data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1)
} else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) {
All_Interaction <- merge(Test_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE)
data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1)
} else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) {
All_Interaction <- merge(Validation_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE)
data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1)
} else if(is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) {
All_Interaction <- Train_Interaction
} else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) {
All_Interaction <- Validation_Interaction
} else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) {
All_Interaction <- Test_Interaction
} else {
All_Interaction <- NULL
}
} else {
All_Interaction <- NULL
}
}
# Evaluation Plots ----
## EvaluationPlots_CalibrationPlot ----
### Test ----
if(!is.null(TestData)) {
Test_EvaluationPlot <- AutoPlots::Plot.Calibration.Line(
dt = TestData,
AggMethod = "mean",
XVar = PredictionColumnName,
YVar = TargetColumnName,
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
NumberBins = 21,
Height = "600px",
Width = "975px",
Title = "Calibration Plot: Test Data",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = "p1",
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)
} else {
Test_EvaluationPlot <- NULL
}
### Train ----
if(!is.null(TrainData)) {
Train_EvaluationPlot <- AutoPlots::Plot.Calibration.Line(
dt = TrainData,
AggMethod = "mean",
XVar = PredictionColumnName,
YVar = TargetColumnName,
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
NumberBins = 21,
Height = "600px",
Width = "975px",
Title = "Calibration Plot: Train Data",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = "p1",
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)
} else {
Train_EvaluationPlot <- NULL
}
## EvaluationPlots_ROC_Plot ----
### TestData ----
if(!is.null(TestData)) {
Test_ROCPlot <- AutoPlots::Plot.ROC(
dt = TestData,
SampleSize = 500000,
XVar = PredictionColumnName,
YVar = TargetColumnName,
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Calibration Plot: Test Data",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = "p1",
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)
} else {
Test_ROCPlot <- NULL
}
### TrainData ----
if(!is.null(TrainData)) {
Train_ROCPlot <- AutoPlots::Plot.ROC(
dt = TrainData,
SampleSize = 500000,
XVar = PredictionColumnName,
YVar = TargetColumnName,
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Calibration Plot: Train Data",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = "p1",
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)
}
## EvaluationPlots_GainsPlots ----
## Test_CumGainsChart ----
if(!is.null(TestData)) {
Test_CumGainsChart <- AutoPlots::Plot.Gains(
dt = TestData,
PreAgg = FALSE,
XVar = PredictionColumnName,
YVar = TargetColumnName,
ZVar = "N",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
NumberBins = 20,
Height = "600px",
Width = "975px",
Title = "Gains Plot: Test Data",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = "p1",
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)
} else {
Test_CumGainsChart <- NULL
}
## Train_CumGainsChart ----
if(!is.null(TrainData)) {
Train_CumGainsChart <- AutoPlots::Plot.Gains(
dt = TrainData,
PreAgg = FALSE,
XVar = PredictionColumnName,
YVar = TargetColumnName,
ZVar = "N",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
NumberBins = 20,
Height = "600px",
Width = "975px",
Title = "Gains Plot: Train Data",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = "p1",
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)
} else {
Train_CumGainsChart <- NULL
}
# Model Interpretation ----
## Model_Evaluation_Metrics_NumericVariables ----
### TestData ----
# Plots to Add and Remove
# Starting batch of plots
Test_ParDepPlots <- list()
# Add Plots
if(!is.null(TestData) && !is.null(FeatureColumnNames)) {
for(g in FeatureColumnNames) {
if(is.numeric(TestData[[g]])) {
Test_ParDepPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Line(
dt = TestData,
XVar = g,
YVar = TargetColumnName,
ZVar = 'p1',
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
GroupVar = NULL,
NumberBins = 20,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Partial Dependence Line",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = g,
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)}, error = function(x) NULL)
}
}
}
### TrainData ----
# Plots to Add and Remove
# Starting batch of plots
Train_ParDepPlots <- list() # ModelObject[['PlotList']][['Train_ParDepPlots']]
# Add Plots
if(!is.null(TrainData) && !is.null(FeatureColumnNames)) {
for(g in FeatureColumnNames) {
if(is.numeric(TrainData[[g]])) {
Train_ParDepPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Line(
dt = TrainData,
XVar = g,
YVar = TargetColumnName,
ZVar = 'p1',
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
GroupVar = NULL,
NumberBins = 20,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Partial Dependence Line",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = g,
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)}, error = function(x) NULL)
}
}
}
## Model_Evaluation_Metrics_NumericVariables_Box ----
### Test Data ----
# Starting batch of plots
Test_ParDepBoxPlots <- list()
# Add Plots
if(!is.null(TestData) && !is.null(FeatureColumnNames)) {
for(g in FeatureColumnNames) {
if(is.numeric(TestData[[g]])) {
Test_ParDepBoxPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Box(
dt = TestData,
XVar = g,
YVar = TargetColumnName,
ZVar = 'p1',
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
GroupVar = NULL,
NumberBins = 20,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Partial Dependence Line",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = g,
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)}, error = function(x) NULL)
}
}
}
### Train Data ----
# Starting batch of plots
Train_ParDepBoxPlots <- list()
# Add Plots
if(!is.null(TrainData) && !is.null(FeatureColumnNames)) {
for(g in FeatureColumnNames) {
if(is.numeric(TrainData[[g]])) {
Train_ParDepBoxPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Box(
dt = TrainData,
XVar = g,
YVar = TargetColumnName,
ZVar = 'p1',
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
GroupVar = NULL,
NumberBins = 20,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Partial Dependence Line",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = g,
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)}, error = function(x) NULL)
}
}
}
## Model_Evaluation_Metrics_CategoricalVariables ----
### Test Data ----
# Starting batch of plots
Test_ParDepCatPlots <- list()
# Add Plots
if(!is.null(TestData) && !is.null(FeatureColumnNames)) {
for(g in FeatureColumnNames) {
if(is.character(TestData[[g]]) || is.factor(TestData[[g]])) {
Test_ParDepCatPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.HeatMap(
dt = TestData,
XVar = g,
YVar = TargetColumnName,
ZVar = 'p1',
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
GroupVar = NULL,
NumberBins = 20,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Partial Dependence Line",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = g,
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)}, error = function(x) NULL)
}
}
}
### Train Data ----
# Starting batch of plots
Train_ParDepCatPlots <- list()
# Add Plots
if(!is.null(TrainData) && !is.null(FeatureColumnNames)) {
for(g in FeatureColumnNames) {
if(is.character(TrainData[[g]]) || is.factor(TrainData[[g]])) {
Train_ParDepCatPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.HeatMap(
dt = TrainData,
XVar = g,
YVar = TargetColumnName,
ZVar = 'p1',
YVarTrans = "Identity",
XVarTrans = "Identity",
ZVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
GroupVar = NULL,
NumberBins = 20,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Partial Dependence Line",
ShowLabels = FALSE,
Title.YAxis = TargetColumnName,
Title.XAxis = g,
EchartsTheme = "wef",
TimeLine = TRUE,
TextColor = "white",
Debug = FALSE)}, error = function(x) NULL)
}
}
}
ML Report Descriptions:
The two main goals with this document are to provide a wide range of output to investigate high level performance and insights, and to deliver a high quality report design layout to increase user experience. The metrics provided are intended to be semi-comprehensive. One can always dig deeper into results to gain further insights. In light of that, the results are intended to provide information that one can come to a reasonable conclusion about their model or to find the areas where they need to dig a little deeper.
Evaluation Metrics
This section contains statistics and variable importance measures to help the user understand model performance at a high level. Train Data results are included and can be used to compare against the Test Data results to identify over / under fitting of models.
Evaluation Plots
This section contains visualizations that span the range of predicted values and the associated accuracies across that range. The predicted values range is broken up into every 5th percentile to provide a wide range for evaluation.
Model Interpretation sections
This section contains visualizations intended to open up the black box of your algorithm. When one inspects coefficients from a regression model, the insights they gain are two-fold:
- get an understanding about statistics significance
- gain an understanding of the variable's effect on the target variable
However, not all relationships are linear and sometimes the user doesn't specifiy an appropriate model structure to fully capture the nature of the relationship, which can lead to incorrect conclusions about both statistical signifance and the nature of the relationship. These visualizations provide a way to understand what the exact nature of those relationships are (in a visual manner) and if the user chooses, they can attempt to fit the relationship more precisely with an appropriate statistical model in order to gain a better understanding of statistical significance.
Evaluation Metrics
Expand
Evaluation Metrics Tables
Model Metrics Tables
TestData
Performance Metrics
if(!is.null(Test_EvalMetrics)) {
reactable::reactable(
width = 1075,
data = Test_EvalMetrics,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('Test_EvalMetrics is NULL')
}
TrainData + ValidationData
Performance Metrics
if(!is.null(Train_EvalMetrics)) {
reactable::reactable(
width = 1075,
data = Train_EvalMetrics,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('Train_EvalMetrics is NULL')
}
Variable Importance Tables
Variable Importance Tables
if(!is.null(All_Importance)) {
data.table::setorderv(x = All_Importance, cols = names(All_Importance)[2L], order = -1L, na.last = TRUE)
reactable::reactable(
width = 1075,
data = All_Importance,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print("No importance was provided")
}
Interaction Importance Tables
Interaction Importance Tables
if(exists("All_Interaction") && !is.null(All_Interaction)) {
data.table::setorderv(x = All_Interaction, cols = names(All_Importance)[3L], order = -1L, na.last = TRUE)
reactable::reactable(
width = 1075,
data = All_Interaction,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('No interaction importance was provided')
}
Evaluation Plots
Expand
Variable Importance Plots
Expand
if(!is.null(Test_Importance)) {
if("Train_Importance" %in% names(Test_Importance)) data.table::setnames(Test_Importance, 'Train_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Test_Importance)) data.table::setnames(Test_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Test_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot: Test Data",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
}
if(!is.null(Validation_Importance)) {
if("Validation_Importance" %in% names(Validation_Importance)) data.table::setnames(Validation_Importance, 'Validation_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Validation_Importance)) data.table::setnames(Validation_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Validation_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot: Validation Data",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
}
if(!is.null(Train_Importance)) {
if("Train_Importance" %in% names(Train_Importance)) data.table::setnames(Train_Importance, 'Train_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Train_Importance)) data.table::setnames(Train_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Train_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot: Train Data",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
}
Calibration Plots
Calibration Plots
if(!is.null(Test_EvaluationPlot)) {
Test_EvaluationPlot
} else {
print('Test_EvaluationPlot is NULL or TestData is NULL')
}
if(!is.null(Train_EvaluationPlot)) {
Train_EvaluationPlot
}
ROC Plots
Expand
if(!is.null(Test_ROCPlot)) {
Test_ROCPlot
} else {
print('Test_ROCPlot is NULL or TestData is NULL')
}
if(!is.null(Train_EvaluationPlot)) {
Train_ROCPlot
}
Lift & Gains Plots
Expand
if(!is.null(Test_CumGainsChart)) {
Test_CumGainsChart
} else {
print('Test_CumGainsChart is NULL or TestData is NULL')
}
if(!is.null(Test_CumGainsChart)) {
Train_CumGainsChart
}
Model Interpretation
Expand
Partial Dependence Plots: Numeric-Features
Expand
Partial Dependence Line Plots
Expand
TestData
Partital Dependence Line Plots
options(warn = -1)
if(!is.null(Test_ParDepPlots) && length(Test_ParDepPlots) > 0) {
echarts4r::e_arrange(Test_ParDepPlots)
} else {
print('Test_ParDepPlots is NULL and TestData is NULL')
}
options(warn = 1)
TrainData + ValidationData
Partital Dependence Line Plots
options(warn = -1)
if(!is.null(Train_ParDepPlots) && length(Train_ParDepPlots) > 0) {
echarts4r::e_arrange(Train_ParDepPlots)
} else {
print('Train Data was not supplied')
}
options(warn = 1)
Partial Partial Dependence Plots: Categorical-Features
Expand
TestData
Partital Dependence Bar Plots
options(warn = -1)
if(!is.null(Test_ParDepCatPlots) && length(Test_ParDepCatPlots) > 0) {
echarts4r::e_arrange(Test_ParDepCatPlots)
} else {
print('Categorical variables were not supplied')
}
options(warn = 1)
TrainData + ValidationData
Partital Dependence Bar Plots
options(warn = -1)
if(!is.null(Train_ParDepCatPlots) && length(Train_ParDepCatPlots) > 0) {
echarts4r::e_arrange(Train_ParDepCatPlots)
} else {
print('Train Data was not supplied')
}
options(warn = 1)
Model MetaData
Expand
Parameters and Settings
Model Parameters
if(!is.null(ArgsList)) {
for(nam in names(ArgsList)) print(paste0(nam, ": ", ArgsList[[nam]]))
} else {
txt <- paste0(ModelID, "_ArgsList.Rdata")
print(paste0('ArgsList is NULL'))
}
Grid Tuning Metrics
Grid Tuning Metrics
if(!is.null(GridMetrics)) {
reactable::reactable(
width = 1075,
data = GridMetrics[order(-MetricValue)],
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = TRUE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print("GridTuning was not conducted")
}
AdrianAntico/ModelingTools documentation built on June 10, 2025, 1:17 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
```{css, echo=FALSE} @import url('https://fonts.googleapis.com/css2?family=Yusei+Magic&display=swap');
- { box-sizing: border-box; }
body { padding: 0; margin: 0; font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 16px; line-height: 1.5; color: #000000; background: linear-gradient(180deg, #787989, #b7b8cf); }
a { color: #b1c5d8; text-decoration: none; } a:hover { text-decoration: underline; }
.page-header { color: #fff; text-align: center; background-color: #000121; background-image: linear-gradient(120deg,#000121,#696b76); padding: 1.5rem 2rem; } .page-header :last-child { margin-bottom: 0.5rem; } @media screen and (max-width: 42em) { .page-header { padding: 1rem 1rem; } }
.project-name { margin-top: 0; margin-bottom: 0.1rem; font-size: 2rem; } @media screen and (max-width: 42em) { .project-name { font-size: 1.75rem; } }
.project-tagline { margin-bottom: 2rem; font-weight: normal; opacity: 0.7; font-size: 1.5rem; } @media screen and (max-width: 42em) { .project-tagline { font-size: 1.2rem; } }
.project-author, .project-date { font-weight: normal; opacity: 0.7; font-size: 1.2rem; } @media screen and (max-width: 42em) { .project-author, .project-date { font-size: 1rem; } }
.main-content, .toc { max-width: 64rem; padding: 2rem 4rem; margin: 0 auto; font-size: 1.1rem; }
.toc { padding-bottom: 0; } .toc .toc-box { padding: 55px; background: linear-gradient(90deg, #000121, #696b76); border: solid 1px #000000; border-radius: 70px; color: white; box-shadow: 8px 5px 10px 0px #000000; } .toc .toc-box .toc-title { margin: 0 0 0.5rem; text-align: center; } .toc .toc-box > ul { margin: 0; padding-left: 1.5rem; } @media screen and (min-width: 42em) and (max-width: 64em) { .toc { padding: 2rem 2rem 0; } } @media screen and (max-width: 42em) { .toc { padding: 2rem 1rem 0; font-size: 1rem; } }
.main-content :first-child { margin-top: 0; } @media screen and (min-width: 42em) and (max-width: 64em) { .main-content { padding: 2rem; } } @media screen and (max-width: 42em) { .main-content { padding: 2rem 1rem; font-size: 1rem; } } .main-content img { max-width: 100%; }
.main-content h1 { margin-top: 2rem; margin-bottom: 1rem; font-weight: bold; color: #070e56; }
.main-content h2 { margin-top: 2rem; margin-bottom: 1rem; font-weight: normal; color: #121e94; } .main-content h3 { margin-top: 2rem; margin-bottom: 1rem; font-weight: normal; color: #3745d1; } .main-content h4 { margin-top: 2rem; margin-bottom: 1rem; font-weight: normal; color: #374995; } .main-content h5 { margin-top: 2rem; margin-bottom: 1rem; font-weight: normal; color: #000000; } .main-content h6 { margin-top: 2rem; margin-bottom: 1rem; font-weight: normal; color: #000000; }
.main-content p { margin-bottom: 1em; } .main-content code { padding: 2px 4px; font-family: Consolas, "Liberation Mono", Menlo, Courier, monospace; color: #2c2c2d; background-color: #f3f6fa; border-radius: 0.3rem; } .main-content pre { padding: 0.8rem; margin-top: 0; margin-bottom: 1rem; font: 1rem Consolas, "Liberation Mono", Menlo, Courier, monospace; color: #2c2c2d; word-wrap: normal; background-color: #e9edf9c9; border: solid 1px #001a35; border-radius: 0.3rem; line-height: 1.45; overflow: auto; } @media screen and (max-width: 42em) { .main-content pre { font-size: 0.9rem; } } .main-content pre > code { padding: 0; margin: 0; color: #2c2c2d; word-break: normal; white-space: pre; background: transparent; border: 0; } @media screen and (max-width: 42em) { .main-content pre > code { font-size: 0.9rem; } } .main-content pre code, .main-content pre tt { display: inline; max-width: initial; padding: 0; margin: 0; overflow: initial; line-height: inherit; word-wrap: normal; background-color: transparent; border: 0; } .main-content pre code:before, .main-content pre code:after, .main-content pre tt:before, .main-content pre tt:after { content: normal; } .main-content ul, .main-content ol { margin-top: 0; } .main-content blockquote { padding: 0 1rem; margin-left: 0; color: #2c2c2d; border-left: 0.3rem solid #dce6f0; font-size: 1.2rem; } .main-content blockquote > :first-child { margin-top: 0; } .main-content blockquote > :last-child { margin-bottom: 0; } @media screen and (max-width: 42em) { .main-content blockquote { font-size: 1.1rem; } } .main-content table { width: 100%; overflow: auto; word-break: normal; word-break: keep-all; -webkit-overflow-scrolling: touch; border-collapse: collapse; border-spacing: 0; margin: 1rem 0; } .main-content table th { font-weight: bold; background-color: #159957; color: #fff; } .main-content table th, .main-content table td { padding: 0.5rem 1rem; border-bottom: 1px solid #e9ebec; text-align: left; } .main-content table tr:nth-child(odd) { background-color: #f2f2f2; } .main-content dl { padding: 0; } .main-content dl dt { padding: 0; margin-top: 1rem; font-size: 1rem; font-weight: bold; } .main-content dl dd { padding: 0; margin-bottom: 1rem; } .main-content hr { height: 2px; padding: 0; margin: 1rem 0; background-color: #eff0f1; border: 0; }
```r if(!is.null(ModelObject)) { # Model MetaData ---- ## Model_MetaData_Parameters ---- ArgsList <- ModelObject[['ArgsList']] ## Model_MetaData_GridMetrics ---- GridMetrics <- ModelObject[['GridMetrics']] }
if(!is.null(ModelObject)) { # DataSets TestData <- ModelObject[['TestData']] ValidationData <- ModelObject[['ValidationData']] TrainData <- ModelObject[['TrainData']] # Meta info TargetColumnName <- ModelObject[['ArgsList']][['TargetColumnName']] PredictionColumnName <- PredictionColumnName if(is.null(PredictionColumnName)) { PredictionColumnName <- ModelObject[['ColNames']][[1L]] } }
if(!is.null(ModelObject)) { # Evaluation Metrics ---- ## Model_Evaluation_Metrics (catboost check, h2o check) ---- Test_EvalMetrics <- ModelObject[['EvaluationMetrics']][['TestData']] Train_EvalMetrics <- ModelObject[['EvaluationMetrics']][['TrainData']] ## Model_VarImportanceTable ---- if(tolower(Algo) == 'catboost') { # Store data Test_Importance <- ModelObject[['VariableImportance']][['Test_Importance']] Validation_Importance <- ModelObject[['VariableImportance']][['Validation_Importance']] Train_Importance <- ModelObject[['VariableImportance']][['Train_Importance']] # Update Colnames if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = 'Importance', new = 'Test_Importance', skip_absent = TRUE) if(!is.null(Validation_Importance)) data.table::setnames(Validation_Importance, old = 'Importance', new = 'Validation_Importance', skip_absent = TRUE) if(!is.null(Train_Importance)) data.table::setnames(Train_Importance, old = 'Importance', new = 'Train_Importance', skip_absent = TRUE) # CatBoost only if(is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- NULL } else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE) All_Importance <- merge(All_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE) } else if(!is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Validation_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- Train_Importance } else if(is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- Validation_Importance } else if(!is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- Test_Importance } else { All_Importance <- NULL } } else { # Store data (xgb check, ) Test_Importance <- ModelObject[['VariableImportance']] Validation_Importance <- NULL Train_Importance <- NULL # Update Colnames if(Algo %in% c("xgboost","lightgbm")) { if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = names(Test_Importance)[2], new = 'Test_Importance', skip_absent = TRUE) } else { # h2o col 3 is scaled importance which is preferred if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = names(Test_Importance)[3], new = 'Test_Importance', skip_absent = TRUE) } # Non CatBoost only if(!is.null(Test_Importance)) { All_Importance <- Test_Importance } else { All_Importance <- NULL } } ## Model_IntImportanceTable ---- if(tolower(Algo) == 'catboost') { Test_Interaction <- ModelObject[['InteractionImportance']][['Test_Interaction']] Validation_Interaction <- ModelObject[['InteractionImportance']][['Validation_Interaction']] Train_Interaction <- ModelObject[['InteractionImportance']][['Train_Interaction']] # Update Colnames if(!is.null(Test_Interaction)) data.table::setnames(Test_Interaction, old = 'score', new = 'Test_Importance', skip_absent = TRUE) if(!is.null(Validation_Interaction)) data.table::setnames(Validation_Interaction, old = 'score', new = 'Validation_Importance', skip_absent = TRUE) if(!is.null(Train_Interaction)) data.table::setnames(Train_Interaction, old = 'score', new = 'Train_Importance', skip_absent = TRUE) # CatBoost only if(is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- NULL } else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE) All_Interaction <- merge(All_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Validation_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- Train_Interaction } else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- Validation_Interaction } else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- Test_Interaction } else { All_Interaction <- NULL } } else { All_Interaction <- NULL } }
# Evaluation Plots ---- ## EvaluationPlots_CalibrationPlot ---- ### Test ---- if(!is.null(TestData)) { Test_EvaluationPlot <- AutoPlots::Plot.Calibration.Line( dt = TestData, AggMethod = "mean", XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 21, Height = "600px", Width = "975px", Title = "Calibration Plot: Test Data", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "p1", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } else { Test_EvaluationPlot <- NULL } ### Train ---- if(!is.null(TrainData)) { Train_EvaluationPlot <- AutoPlots::Plot.Calibration.Line( dt = TrainData, AggMethod = "mean", XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 21, Height = "600px", Width = "975px", Title = "Calibration Plot: Train Data", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "p1", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } else { Train_EvaluationPlot <- NULL } ## EvaluationPlots_ROC_Plot ---- ### TestData ---- if(!is.null(TestData)) { Test_ROCPlot <- AutoPlots::Plot.ROC( dt = TestData, SampleSize = 500000, XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Calibration Plot: Test Data", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "p1", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } else { Test_ROCPlot <- NULL } ### TrainData ---- if(!is.null(TrainData)) { Train_ROCPlot <- AutoPlots::Plot.ROC( dt = TrainData, SampleSize = 500000, XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Calibration Plot: Train Data", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "p1", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } ## EvaluationPlots_GainsPlots ---- ## Test_CumGainsChart ---- if(!is.null(TestData)) { Test_CumGainsChart <- AutoPlots::Plot.Gains( dt = TestData, PreAgg = FALSE, XVar = PredictionColumnName, YVar = TargetColumnName, ZVar = "N", GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 20, Height = "600px", Width = "975px", Title = "Gains Plot: Test Data", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "p1", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } else { Test_CumGainsChart <- NULL } ## Train_CumGainsChart ---- if(!is.null(TrainData)) { Train_CumGainsChart <- AutoPlots::Plot.Gains( dt = TrainData, PreAgg = FALSE, XVar = PredictionColumnName, YVar = TargetColumnName, ZVar = "N", GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 20, Height = "600px", Width = "975px", Title = "Gains Plot: Train Data", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "p1", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } else { Train_CumGainsChart <- NULL }
# Model Interpretation ---- ## Model_Evaluation_Metrics_NumericVariables ---- ### TestData ---- # Plots to Add and Remove # Starting batch of plots Test_ParDepPlots <- list() # Add Plots if(!is.null(TestData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TestData[[g]])) { Test_ParDepPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Line( dt = TestData, XVar = g, YVar = TargetColumnName, ZVar = 'p1', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE)}, error = function(x) NULL) } } } ### TrainData ---- # Plots to Add and Remove # Starting batch of plots Train_ParDepPlots <- list() # ModelObject[['PlotList']][['Train_ParDepPlots']] # Add Plots if(!is.null(TrainData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TrainData[[g]])) { Train_ParDepPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Line( dt = TrainData, XVar = g, YVar = TargetColumnName, ZVar = 'p1', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE)}, error = function(x) NULL) } } } ## Model_Evaluation_Metrics_NumericVariables_Box ---- ### Test Data ---- # Starting batch of plots Test_ParDepBoxPlots <- list() # Add Plots if(!is.null(TestData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TestData[[g]])) { Test_ParDepBoxPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Box( dt = TestData, XVar = g, YVar = TargetColumnName, ZVar = 'p1', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE)}, error = function(x) NULL) } } } ### Train Data ---- # Starting batch of plots Train_ParDepBoxPlots <- list() # Add Plots if(!is.null(TrainData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TrainData[[g]])) { Train_ParDepBoxPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.Box( dt = TrainData, XVar = g, YVar = TargetColumnName, ZVar = 'p1', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE)}, error = function(x) NULL) } } } ## Model_Evaluation_Metrics_CategoricalVariables ---- ### Test Data ---- # Starting batch of plots Test_ParDepCatPlots <- list() # Add Plots if(!is.null(TestData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.character(TestData[[g]]) || is.factor(TestData[[g]])) { Test_ParDepCatPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.HeatMap( dt = TestData, XVar = g, YVar = TargetColumnName, ZVar = 'p1', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE)}, error = function(x) NULL) } } } ### Train Data ---- # Starting batch of plots Train_ParDepCatPlots <- list() # Add Plots if(!is.null(TrainData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.character(TrainData[[g]]) || is.factor(TrainData[[g]])) { Train_ParDepCatPlots[[g]] <- tryCatch({AutoPlots::Plot.PartialDependence.HeatMap( dt = TrainData, XVar = g, YVar = TargetColumnName, ZVar = 'p1', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE)}, error = function(x) NULL) } } }
ML Report Descriptions:
The two main goals with this document are to provide a wide range of output to investigate high level performance and insights, and to deliver a high quality report design layout to increase user experience. The metrics provided are intended to be semi-comprehensive. One can always dig deeper into results to gain further insights. In light of that, the results are intended to provide information that one can come to a reasonable conclusion about their model or to find the areas where they need to dig a little deeper.
Evaluation Metrics
This section contains statistics and variable importance measures to help the user understand model performance at a high level. Train Data results are included and can be used to compare against the Test Data results to identify over / under fitting of models.
Evaluation Plots
This section contains visualizations that span the range of predicted values and the associated accuracies across that range. The predicted values range is broken up into every 5th percentile to provide a wide range for evaluation.
Model Interpretation sections
This section contains visualizations intended to open up the black box of your algorithm. When one inspects coefficients from a regression model, the insights they gain are two-fold: - get an understanding about statistics significance - gain an understanding of the variable's effect on the target variable However, not all relationships are linear and sometimes the user doesn't specifiy an appropriate model structure to fully capture the nature of the relationship, which can lead to incorrect conclusions about both statistical signifance and the nature of the relationship. These visualizations provide a way to understand what the exact nature of those relationships are (in a visual manner) and if the user chooses, they can attempt to fit the relationship more precisely with an appropriate statistical model in order to gain a better understanding of statistical significance.
Evaluation Metrics
Expand
Evaluation Metrics Tables
Model Metrics Tables
TestData
Performance Metrics
if(!is.null(Test_EvalMetrics)) {
reactable::reactable(
width = 1075,
data = Test_EvalMetrics,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('Test_EvalMetrics is NULL')
}
TrainData + ValidationData
Performance Metrics
if(!is.null(Train_EvalMetrics)) {
reactable::reactable(
width = 1075,
data = Train_EvalMetrics,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('Train_EvalMetrics is NULL')
}
Variable Importance Tables
Variable Importance Tables
if(!is.null(All_Importance)) {
data.table::setorderv(x = All_Importance, cols = names(All_Importance)[2L], order = -1L, na.last = TRUE)
reactable::reactable(
width = 1075,
data = All_Importance,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print("No importance was provided")
}
Interaction Importance Tables
Interaction Importance Tables
if(exists("All_Interaction") && !is.null(All_Interaction)) {
data.table::setorderv(x = All_Interaction, cols = names(All_Importance)[3L], order = -1L, na.last = TRUE)
reactable::reactable(
width = 1075,
data = All_Interaction,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('No interaction importance was provided')
}
Evaluation Plots
Expand
Variable Importance Plots
Expand
if(!is.null(Test_Importance)) {
if("Train_Importance" %in% names(Test_Importance)) data.table::setnames(Test_Importance, 'Train_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Test_Importance)) data.table::setnames(Test_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Test_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot: Test Data",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
}
if(!is.null(Validation_Importance)) {
if("Validation_Importance" %in% names(Validation_Importance)) data.table::setnames(Validation_Importance, 'Validation_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Validation_Importance)) data.table::setnames(Validation_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Validation_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot: Validation Data",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
}
if(!is.null(Train_Importance)) {
if("Train_Importance" %in% names(Train_Importance)) data.table::setnames(Train_Importance, 'Train_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Train_Importance)) data.table::setnames(Train_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Train_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot: Train Data",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
}
Calibration Plots
Calibration Plots
if(!is.null(Test_EvaluationPlot)) {
Test_EvaluationPlot
} else {
print('Test_EvaluationPlot is NULL or TestData is NULL')
}
if(!is.null(Train_EvaluationPlot)) {
Train_EvaluationPlot
}
ROC Plots
Expand
if(!is.null(Test_ROCPlot)) {
Test_ROCPlot
} else {
print('Test_ROCPlot is NULL or TestData is NULL')
}
if(!is.null(Train_EvaluationPlot)) {
Train_ROCPlot
}
Lift & Gains Plots
Expand
if(!is.null(Test_CumGainsChart)) {
Test_CumGainsChart
} else {
print('Test_CumGainsChart is NULL or TestData is NULL')
}
if(!is.null(Test_CumGainsChart)) {
Train_CumGainsChart
}
Model Interpretation
Expand
Partial Dependence Plots: Numeric-Features
Expand
Partial Dependence Line Plots
Expand
TestData
Partital Dependence Line Plots
options(warn = -1)
if(!is.null(Test_ParDepPlots) && length(Test_ParDepPlots) > 0) {
echarts4r::e_arrange(Test_ParDepPlots)
} else {
print('Test_ParDepPlots is NULL and TestData is NULL')
}
options(warn = 1)
TrainData + ValidationData
Partital Dependence Line Plots
options(warn = -1)
if(!is.null(Train_ParDepPlots) && length(Train_ParDepPlots) > 0) {
echarts4r::e_arrange(Train_ParDepPlots)
} else {
print('Train Data was not supplied')
}
options(warn = 1)
Partial Partial Dependence Plots: Categorical-Features
Expand
TestData
Partital Dependence Bar Plots
options(warn = -1)
if(!is.null(Test_ParDepCatPlots) && length(Test_ParDepCatPlots) > 0) {
echarts4r::e_arrange(Test_ParDepCatPlots)
} else {
print('Categorical variables were not supplied')
}
options(warn = 1)
TrainData + ValidationData
Partital Dependence Bar Plots
options(warn = -1)
if(!is.null(Train_ParDepCatPlots) && length(Train_ParDepCatPlots) > 0) {
echarts4r::e_arrange(Train_ParDepCatPlots)
} else {
print('Train Data was not supplied')
}
options(warn = 1)
Model MetaData
Expand
Parameters and Settings
Model Parameters
if(!is.null(ArgsList)) {
for(nam in names(ArgsList)) print(paste0(nam, ": ", ArgsList[[nam]]))
} else {
txt <- paste0(ModelID, "_ArgsList.Rdata")
print(paste0('ArgsList is NULL'))
}
Grid Tuning Metrics
Grid Tuning Metrics
if(!is.null(GridMetrics)) {
reactable::reactable(
width = 1075,
data = GridMetrics[order(-MetricValue)],
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = TRUE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print("GridTuning was not conducted")
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.