View source: R/ModelEvaluationPlots.R
EvalPlot | R Documentation |
This function automatically builds calibration plots and calibration boxplots for model evaluation using regression, quantile regression, and binary and multinomial classification
EvalPlot(
data,
PredictionColName = c("PredictedValues"),
TargetColName = c("ActualValues"),
GraphType = c("calibration"),
PercentileBucket = 0.05,
aggrfun = function(x) mean(x, na.rm = TRUE)
)
data |
Data containing predicted values and actual values for comparison |
PredictionColName |
String representation of column name with predicted values from model |
TargetColName |
String representation of column name with target values from model |
GraphType |
Calibration or boxplot - calibration aggregated data based on summary statistic; boxplot shows variation |
PercentileBucket |
Number of buckets to partition the space on (0,1) for evaluation |
aggrfun |
The statistics function used in aggregation, listed as a function |
Calibration plot or boxplot
Adrian Antico
Other Model Evaluation and Interpretation:
AutoShapeShap()
,
CumGainsChart()
,
ParDepCalPlots()
,
ROCPlot()
,
RedYellowGreen()
,
ResidualPlots()
,
SingleRowShapeShap()
,
threshOptim()
## Not run:
# Create fake data
data <- AutoQuant::FakeDataGenerator(
Correlation = 0.70, N = 10000000, Classification = TRUE)
data.table::setnames(data, "IDcol_1", "Predict")
# Run function
AutoQuant::EvalPlot(
data,
PredictionColName = "Predict",
TargetColName = "Adrian",
GraphType = "calibration",
PercentileBucket = 0.05,
aggrfun = function(x) mean(x, na.rm = TRUE))
## End(Not run)
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