Man pages for daltoolbox
Leveraging Experiment Lines to Data Analytics
| action | Action |
| action.dal_transform | Action implementation for transform |
| adjust_class_label | adjust categorical mapping |
| adjust_data.frame | Adjust to data frame |
| adjust_factor | adjust factors |
| adjust_matrix | adjust to matrix |
| adjust_ts_data | adjust 'ts_data' |
| autoenc_encode | Autoencoder - Encode |
| autoenc_encode_decode | Autoencoder - Encode |
| Boston | Boston Housing Data (Regression) |
| categ_mapping | Categorical mapping |
| cla_dtree | Decision Tree for classification |
| cla_knn | K Nearest Neighbor Classification |
| cla_majority | Majority Classification |
| cla_mlp | MLP for classification |
| cla_nb | Naive Bayes Classifier |
| cla_rf | Random Forest for classification |
| classification | classification |
| cla_svm | SVM for classification |
| cla_tune | Classification Tune |
| cluster | Cluster |
| cluster_dbscan | DBSCAN |
| clusterer | Clusterer |
| cluster_kmeans | k-means |
| cluster_pam | PAM |
| clu_tune | Clustering Tune |
| dal_base | Class dal_base |
| dal_learner | DAL Learner |
| dal_transform | DAL Transform |
| dal_tune | DAL Tune |
| data_sample | Data Sample |
| do_fit | do fit for time series |
| do_predict | do predict for time series |
| dt_pca | PCA |
| evaluate | evaluate |
| fit | Fit |
| fit.cla_tune | tune hyperparameters of ml model |
| fit.cluster_dbscan | fit dbscan model |
| fit_curvature_max | maximum curvature analysis |
| fit_curvature_min | minimum curvature analysis |
| inverse_transform | Inverse Transform |
| k_fold | k-fold sampling |
| minmax | min-max normalization |
| MSE.ts | MSE |
| outliers | Outliers |
| plot_bar | plot bar graph |
| plot_boxplot | plot boxplot |
| plot_boxplot_class | plot boxplot per class |
| plot_density | plot density |
| plot_density_class | plot density per class |
| plot_groupedbar | plot grouped bar |
| plot_hist | plot histogram |
| plot_lollipop | plot lollipop |
| plot_pieplot | plot pie |
| plot_points | plot points |
| plot_radar | plot radar |
| plot_scatter | scatter graph |
| plot_series | plot series |
| plot_stackedbar | plot stacked bar |
| plot_ts | Plot a time series chart |
| plot_ts_pred | Plot a time series chart |
| predictor | DAL Predict |
| R2.ts | R2 |
| reg_dtree | Decision Tree for regression |
| reg_knn | knn regression |
| reg_mlp | MLP for regression |
| regression | Regression |
| reg_rf | Random Forest for regression |
| reg_svm | SVM for regression |
| reg_tune | Regression Tune |
| sample_random | Sample Random |
| sample_stratified | sample_stratified |
| select_hyper | Selection hyper parameters |
| select_hyper.cla_tune | selection of hyperparameters |
| select_hyper.ts_tune | selection of hyperparameters (time series) |
| set_params | Assign parameters |
| set_params.default | Assign parameters |
| sin_data | Time series example dataset |
| sMAPE.ts | sMAPE |
| smoothing | Smoothing |
| smoothing_cluster | Smoothing by cluster |
| smoothing_freq | Smoothing by Freq |
| smoothing_inter | Smoothing by interval |
| sub-.ts_data | Extract a subset of a time series stored in an object |
| train_test | training and test |
| train_test_from_folds | k-fold training and test partition object |
| transform | Transform |
| ts_arima | ARIMA |
| ts_conv1d | Conv1D |
| ts_data | ts_data |
| ts_elm | ELM |
| ts_head | ts_head |
| ts_knn | knn time series prediction |
| ts_lstm | LSTM |
| ts_mlp | MLP |
| ts_norm_an | Time Series Adaptive Normalization |
| ts_norm_diff | Time Series Diff |
| ts_norm_ean | Time Series Adaptive Normalization (Exponential Moving... |
| ts_norm_gminmax | Time Series Global Min-Max |
| ts_norm_swminmax | Time Series Sliding Window Min-Max |
| ts_projection | Time Series Projection |
| ts_reg | TSReg |
| ts_regsw | TSRegSW |
| ts_rf | Random Forest |
| ts_sample | Time Series Sample |
| ts_svm | SVM |
| ts_tune | Time Series Tune |
| zscore | z-score normalization |
