mplot_full | R Documentation |
This function plots a whole dashboard with a model's results. It will automatically detect if it's a categorical or regression's model by checking how many different unique values the dependent variable (tag) has.
mplot_full(
tag,
score,
multis = NA,
splits = 8,
thresh = 6,
subtitle = NA,
model_name = NA,
plot = TRUE,
save = FALSE,
subdir = NA,
file_name = "viz_full.png"
)
tag |
Vector. Real known label. |
score |
Vector. Predicted value or model's result. |
multis |
Data.frame. Containing columns with each category probability or score (only used when more than 2 categories coexist). |
splits |
Integer. Number of separations to plot |
thresh |
Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification) |
subtitle |
Character. Subtitle to show in plot |
model_name |
Character. Model's name |
plot |
Boolean. Plot results? If not, plot grid object returned |
save |
Boolean. Save output plot into working directory |
subdir |
Character. Sub directory on which you wish to save the plot |
file_name |
Character. File name as you wish to save the plot |
Multiple plots gathered into one, showing tag
vs
score
performance results.
Other ML Visualization:
mplot_conf()
,
mplot_cuts()
,
mplot_cuts_error()
,
mplot_density()
,
mplot_gain()
,
mplot_importance()
,
mplot_lineal()
,
mplot_metrics()
,
mplot_response()
,
mplot_roc()
,
mplot_splits()
,
mplot_topcats()
Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)
# Dasboard for Binomial Model
mplot_full(dfr$class2$tag, dfr$class2$scores,
model_name = "Titanic Survived Model"
)
# Dasboard for Multi-Categorical Model
mplot_full(dfr$class3$tag, dfr$class3$score,
multis = subset(dfr$class3, select = -c(tag, score)),
model_name = "Titanic Class Model"
)
# Dasboard for Regression Model
mplot_full(dfr$regr$tag, dfr$regr$score,
model_name = "Titanic Fare Model"
)
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