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_get_confusion |
Compute the confusion matrix for the predict result. |
mi_get_ID |
Get ID data from 'Biomart' database use 'attributes'. |
mi_get_ID_attr |
Get ID attributes from 'Biomart' database. |
mi_get_miss |
Observe the distribution of the false response of 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 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 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 Random Forest model by hyperband. |
mi_tune_rp |
Tune decision tree model by hyperband. |
mi_tune_xgb |
Tune Xgboost model by hyperband. |