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