Generate variable importance plots
Plot.VariableImportance(
dt = NULL,
XVar = NULL,
YVar = NULL,
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = NULL,
Width = NULL,
Title = "Variable Importance Plot",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "macarons",
TimeLine = TRUE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE
)
plot
source data.table
Column name of X-Axis variable. If NULL then ignored
Column name of Y-Axis variable. If NULL then ignored
Column name of Group Variable for distinct colored histograms by group levels
"Asinh", "Log", "LogPlus1", "Sqrt", "Asin", "Logit", "PercRank", "Standardize", "BoxCox", "YeoJohnson"
"Asinh", "Log", "LogPlus1", "Sqrt", "Asin", "Logit", "PercRank", "Standardize", "BoxCox", "YeoJohnson"
Defaults to 1 which causes no faceting to occur vertically. Otherwise, supply a numeric value for the number of output grid rows
Defaults to 1 which causes no faceting to occur horizontally. Otherwise, supply a numeric value for the number of output grid columns
Faceting rows x columns is the max number of levels allowed in a grid. If your GroupVar has more you can supply the levels to display.
Choose from 'mean', 'sum', 'sd', and 'median'
"400px"
"200px"
title
character
character
character
"auritus","azul","bee-inspired","blue","caravan","carp","chalk","cool","dark-bold","dark","eduardo", #' "essos","forest","fresh-cut","fruit","gray","green","halloween","helianthus","infographic","inspired", #' "jazz","london","dark","macarons","macarons2","mint","purple-passion","red-velvet","red","roma","royal", #' "sakura","shine","tech-blue","vintage","walden","wef","weforum","westeros","wonderland"
logical
'darkblue'
22
"bold"
'#63aeff'
3
1
-1
14
14
Debugging purposes
Adrian Antico
Other Model Evaluation:
Plot.BinaryMetrics()
,
Plot.Calibration.Box()
,
Plot.Calibration.Line()
,
Plot.ConfusionMatrix()
,
Plot.Gains()
,
Plot.Lift()
,
Plot.PartialDependence.Box()
,
Plot.PartialDependence.HeatMap()
,
Plot.PartialDependence.Line()
,
Plot.ROC()
,
Plot.Residuals.Histogram()
,
Plot.Residuals.Scatter()
,
Plot.ShapImportance()