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. |

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