EvalPlot: EvalPlot

In AdrianAntico/RemixAutoML: AutoQuant

EvalPlot

Description

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

Usage

EvalPlot(
  data,
  PredictionColName = c("PredictedValues"),
  TargetColName = c("ActualValues"),
  GraphType = c("calibration"),
  PercentileBucket = 0.05,
  aggrfun = function(x) mean(x, na.rm = TRUE)
)

Arguments

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

Value

Calibration plot or boxplot

Author(s)

Adrian Antico

See Also

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

Examples

## 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)

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