plot_variable_importances_processed: Plot random forest variable importances scores.

In easyml: Easily Build and Evaluate Machine Learning Models

Description

When calling easy_random_forest, variable importances scores from the generate_variable_importances output are processed by the process_variable_importances function and generated into a plot. Importance scores for each predictor were estimated using the increase in node impurity. Node impurity measures the change in residual squared error that is attributable to the predictor across all trees. Unlike the easy_glmnet coefficients, random forest importance scores do not indicate directional effects, but instead represent the magnitude of the effect that the predictor has on overall prediction accuracy. Be careful using this plotting method with datasets containing more than 20 variables as the plot may not render as nicely.

Usage

plot_variable_importances_processed(variable_importances_processed)

Arguments

variable_importances_processed

A data.frame, the output of the function process_variable_importances.

Value

A ggplot object. This plot may be rendered by outputting it to the command line or modified using ggplot semantics.

See Also

Other plot: plot_coefficients_processed, plot_model_performance_binomial_auc_score, plot_model_performance_gaussian_correlation_score, plot_model_performance_gaussian_mse_score, plot_model_performance_gaussian_r2_score, plot_model_performance_histogram, plot_predictions_binomial, plot_predictions_gaussian, plot_roc_curve

easyml documentation built on June 26, 2017, 9:02 a.m.