f_predict_plot_model_performance_regression: plot model performance

Description Usage Arguments Value See Also Examples

View source: R/f_predict.R

Description

add predictions to modelling dataframe and unnest, create a title column and a bins column

Usage

1

Arguments

data

dataframe with the columns title, bins, resid_abs, resid_squ, ape

Value

taglist

[1]

Headline summary Plots

[2]

Residuals Pointplot

[3]

Residuals Boxplot

[4]

APE Pointplot

[5]

APE Boxplot

[6]

MAPE, MSE, MAE, Binning

[7]

Headline Performance Measures Summary

[8]

Summary MAPE, MSE, MAE with SE

[9]

Summary MAPE, MSE, MAE with CI95

[10]

Headline Summary Tables

[11]

Summary Table

[12]

Table Binning

See Also

tagList ggplotly datatable f_predict_pl_regression

Examples

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## Not run: 
form = as.formula( 'displacement~cylinders+mpg')

ISLR::Auto %>%
 pipelearner::pipelearner() %>%
 pipelearner::learn_models( rpart::rpart, form ) %>%
 pipelearner::learn_models( randomForest::randomForest, form ) %>%
 pipelearner::learn_models( e1071::svm, form ) %>%
 pipelearner::learn() %>%
 f_predict_pl_regression( 'name' ) %>%
 unnest(preds) %>%
 mutate( bins = cut(target1, breaks = 3 , dig.lab = 4)
         , title = paste(models.id, cv_pairs.id, train_p, target, model) ) %>%
 f_predict_plot_model_performance_regression() %>%
 f_plot_obj_2_html(type = 'taglist', 'test_me', title = 'Model Performance')

file.remove('test_me.html')

## End(Not run)

erblast/oetteR documentation built on Aug. 4, 2018, 11:03 p.m.