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This function lets us split and compare quantiles on a given prediction to compare different categorical values vs scores grouped by equal sized buckets.
mplot_splits(
tag,
score,
splits = 5,
subtitle = NA,
model_name = NA,
save = FALSE,
subdir = NA,
file_name = "viz_splits.png"
)
Vector. Real known label.
Vector. Predicted value or model's result.
Integer. Number of separations to plot
Character. Subtitle to show in plot
Character. Model's name
Boolean. Save output plot into working directory
Character. Sub directory on which you wish to save the plot
Character. File name as you wish to save the plot
Plot with distribution and performance results by splits.
Other ML Visualization:
mplot_conf()
,
mplot_cuts_error()
,
mplot_cuts()
,
mplot_density()
,
mplot_full()
,
mplot_gain()
,
mplot_importance()
,
mplot_lineal()
,
mplot_metrics()
,
mplot_response()
,
mplot_roc()
,
mplot_topcats()
# NOT RUN {
Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)
# For categorical (binary) values
mplot_splits(dfr$class2$tag, dfr$class2$scores,
splits = 4,
model_name = "Titanic Survived Model"
)
# For categorical (+2) values
mplot_splits(dfr$class3$tag, dfr$class2$scores,
model_name = "Titanic Class Model"
)
# For continuous values
mplot_splits(dfr$regr$tag, dfr$regr$score,
splits = 4,
model_name = "Titanic Fare Model"
)
# }
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