Man pages for easyml
Easily Build and Evaluate Machine Learning Models

cocaine_dependence	Cocaine data.
correlation_test	Compute the matrix of p-value.
easy_analysis	The core recipe of easyml.
easy_avNNet	Easily build and evaluate an average neural network model.
easy_deep_neural_network	Easily build and evaluate a deep neural network.
easy_glinternet	Easily build and evaluate a penalized regression model with...
easy_glmnet	Easily build and evaluate a penalized regression model.
easyml	easyml: Easily build and evaluate machine learning models.
easy_neural_network	Easily build and evaluate a neural network.
easy_random_forest	Easily build and evaluate a random forest regression model.
easy_support_vector_machine	Easily build and evaluate a support vector machine regression...
extract_coefficients	Extract coefficients.
extract_coefficients.easy_glmnet	Extract coefficients from a penalized regression model.
extract_variable_importances	Extract variable importances.
extract_variable_importances.easy_random_forest	Extract variable importance scores from a random forest...
fit_model	Fit model.
fit_model.easy_avNNet	Fit an average neural network model.
fit_model.easy_deep_neural_network	Fit a deep neural network model.
fit_model.easy_glinternet	Fit a penalized regression model with interactions.
fit_model.easy_glmnet	Fit a penalized regression model.
fit_model.easy_neural_network	Fit a neural network model.
fit_model.easy_random_forest	Fit a random forest model.
fit_model.easy_support_vector_machine	Fit a support vector machine regression model.
generate_coefficients	Generate coefficients for a model (if applicable).
generate_model_performance	Generate measures of model performance for a model.
generate_predictions	Generate predictions for a model.
generate_variable_importances	Generate variable importances for a model (if applicable).
measure_auc_score	Measure area under the curve.
measure_correlation_score	Measure Pearsons Correlation Coefficient.
measure_mse_score	Measure mean squared error.
measure_r2_score	Measure Coefficient of Determination (R^2 Score).
plot_coefficients_processed	Plot penalized regression coefficients.
plot_model_performance_binomial_auc_score	Plot histogram of the area under the curve (AUC) metrics.
plot_model_performance_gaussian_correlation_score	Plot histogram of the correlation coefficient metrics.
plot_model_performance_gaussian_mse_score	Plot histogram of the mean squared error metrics.
plot_model_performance_gaussian_r2_score	Plot histogram of the coefficient of determination (R^2)...
plot_model_performance_histogram	Plot histogram of measures of model performance.
plot_predictions_binomial	Plot binomial predictions.
plot_predictions_gaussian	Plot gaussian predictions.
plot_roc_curve	Plot ROC Curve.
plot_variable_importances_processed	Plot random forest variable importances scores.
predict_model	Predict model.
predict_model.easy_avNNet	Predict values for an average neural network model.
predict_model.easy_deep_neural_network	Predict values for a deep neural network model.
predict_model.easy_glinternet	Predict values for a penalized regression model with...
predict_model.easy_glmnet	Predict values for a penalized regression model.
predict_model.easy_neural_network	Predict values for a neural network model.
predict_model.easy_random_forest	Predict values for a random forest regression model.
predict_model.easy_support_vector_machine	Predict values for a support vector machine regression model.
preprocess_identity	Preprocess data by leaving it exactly the way it is.
preprocess_scale	Preprocess data by scaling it.
process_coefficients	Process coefficients.
process_variable_importances	Process variable importances.
prostate	Prostate data.
reduce_cores	Reduce number of cores.
remove_variables	Remove variables from a dataset.
resample_fold_train_test_split	Sample with respect to an identification vector
resample_simple_train_test_split	Train test split.
resample_stratified_class_train_test_split	Sample in equal proportion.
resample_stratified_simple_train_test_split	Sample in equal proportion.
set_categorical_variables	Set categorical variables.
set_column_names	Set column names.
set_cores	Set cores.
set_dependent_variable	Set dependent variable.
set_independent_variables	Set independent variables.
set_looper	Set looper.
set_looper_	Set looper.
set_measure	Set measure function.
set_parallel	Set parallel.
set_plot_model_performance	Set plot model performance function.
set_plot_predictions	Set plot predictions function.
set_preprocess	Set preprocess function.
set_random_state	Set random state.
set_resample	Set resample function.