ParDepCalPlots: ParDepCalPlots

In AdrianAntico/RemixAutoML: AutoQuant

ParDepCalPlots

Description

This function automatically builds partial dependence calibration plots and partial dependence calibration boxplots for model evaluation using regression, quantile regression, and binary and multinomial classification

Usage

ParDepCalPlots(
  data,
  PredictionColName = NULL,
  TargetColName = NULL,
  IndepVar = NULL,
  GraphType = "calibration",
  PercentileBucket = 0.05,
  FactLevels = 10,
  Function = function(x) mean(x, na.rm = TRUE),
  DateColumn = NULL,
  DateAgg_3D = NULL,
  PlotYMeanColor = "black",
  PlotXMeanColor = "chocolate",
  PlotXLowColor = "purple",
  PlotXHighColor = "purple"
)

Arguments

data	Data containing predicted values and actual values for comparison
PredictionColName	Predicted values column names
TargetColName	Target value column names
IndepVar	Independent variable column names
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
FactLevels	The number of levels to show on the chart (1. Levels are chosen based on frequency; 2. all other levels grouped and labeled as "Other")
Function	Supply the function you wish to use for aggregation.
DateColumn	Add date column for 3D scatterplot
DateAgg_3D	Aggregate date column by 'day', 'week', 'month', 'quarter', 'year'

Value

Partial dependence calibration plot or boxplot

Author(s)

Adrian Antico

See Also

Other Model Evaluation and Interpretation: AutoShapeShap(), CumGainsChart(), EvalPlot(), ROCPlot(), RedYellowGreen(), ResidualPlots(), SingleRowShapeShap(), threshOptim()

Examples

## Not run: 
# Create fake data
data <- AutoQuant::FakeDataGenerator(
  Correlation = 0.70, N = 10000000, Classification = FALSE)
data.table::setnames(data, "Independent_Variable2", "Predict")

# Build plot
Plot <- AutoQuant::ParDepCalPlots(
  data,
  PredictionColName = "Predict",
  TargetColName = "Adrian",
  IndepVar = "Independent_Variable1",
  GraphType = "calibration",
  PercentileBucket = 0.20,
  FactLevels = 10,
  Function = function(x) mean(x, na.rm = TRUE),
  DateColumn = NULL,
  DateAgg_3D = NULL)

# Step through function
# PredictionColName = "Predict"
# TargetColName = "Adrian"
# IndepVar = "Independent_Variable1"
# GraphType = "calibration"
# PercentileBucket = 0.20
# FactLevels = 10
# Function = function(x) mean(x, na.rm = TRUE)
# DateColumn = NULL
# DateAgg_3D = NULL

## End(Not run)

AdrianAntico/RemixAutoML documentation built on Feb. 3, 2024, 3:32 a.m.