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. |
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