Description Usage Arguments Value See Also Examples
add predictions to modelling dataframe and unnest, create a title column and a bins column
1 |
data |
dataframe with the columns title, bins, resid_abs, resid_squ, ape |
taglist
Headline summary Plots
Residuals Pointplot
Residuals Boxplot
APE Pointplot
APE Boxplot
MAPE, MSE, MAE, Binning
Headline Performance Measures Summary
Summary MAPE, MSE, MAE with SE
Summary MAPE, MSE, MAE with CI95
Headline Summary Tables
Summary Table
Table Binning
tagList
ggplotly
datatable
f_predict_pl_regression
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## 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)
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