Example                 ID example dataset.
mi                      A wrapper function that executes MantaID
                        workflow.
mi_balance_data         Data balance. Most classes adopt random
                        undersampling, while a few classes adopt smote
                        method to oversample to obtain relatively
                        balanced data.
mi_clean_data           Reshape data and delete meaningless rows.
mi_data_attributes      ID-related datasets in biomart.
mi_data_procID          Processed ID data.
mi_data_rawID           ID dataset for testing.
mi_filter_feat          Performing feature selection in a automatic way
                        based on correlation and feature importance.
mi_get_ID               Get ID data from the 'Biomart' database using
                        'attributes'.
mi_get_ID_attr          Get ID attributes from the 'Biomart' database.
mi_get_confusion        Compute the confusion matrix for the predicted
                        result.
mi_get_importance       Plot the bar plot for feature importance.
mi_get_miss             Observe the distribution of the false response
                        of the test set.
mi_get_padlen           Get max length of ID data.
mi_plot_cor             Plot correlation heatmap.
mi_plot_heatmap         Plot heatmap for result confusion matrix.
mi_predict_new          Predict new data with a trained learner.
mi_run_bmr              Compare classification models with small
                        samples.
mi_split_col            Cut the string of ID column character by
                        character and divide it into multiple columns.
mi_split_str            Split the string into individual characters and
                        complete the character vector to the maximum
                        length.
mi_to_numer             Convert data to numeric, and for the ID column
                        convert with fixed levels.
mi_train_BP             Train a three layers neural network model.
mi_train_rg             Random Forest Model Training.
mi_train_rp             Classification tree model training.
mi_train_xgb            Xgboost model training
mi_tune_rg              Tune the Random Forest model by hyperband.
mi_tune_rp              Tune the Decision Tree model by hyperband.
mi_tune_xgb             Tune the Xgboost model by hyperband.
mi_unify_mod            Predict with four models and unify results by
                        the sub-model's specificity score to the four
                        possible classes.
