Leveraging Experiment Lines to Data Analytics
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Documentation for package ‘daltoolbox’ version 1.3.717
Help Pages
A B C D E F H I K M N O P R S T Z
-- A --
| 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 |
| aggregation | Aggregation by groups |
| autoenc_base_e | Autoencoder base (encoder) |
| autoenc_base_ed | Autoencoder base (encoder + decoder) |
-- B --
| Boston | Boston Housing Data (Regression) |
-- C --
| categ_mapping | Categorical mapping (one‑hot encoding) |
| classification | Classification base class |
| cla_bagging | Bagging (ipred) |
| cla_boosting | Boosting (adabag) |
| cla_dtree | Decision Tree for classification |
| cla_glm | Logistic regression (GLM) |
| cla_glmnet | LASSO logistic regression (glmnet) |
| cla_knn | K-Nearest Neighbors (KNN) Classification |
| cla_majority | Majority baseline classifier |
| cla_mlp | MLP for classification |
| cla_multinom | Multinomial logistic regression |
| cla_nb | Naive Bayes Classifier |
| cla_rf | Random Forest for classification |
| cla_rpart | CART (rpart) |
| cla_svm | SVM for classification |
| cla_tune | Classification tuning (k-fold CV) |
| cla_xgboost | XGBoost |
| cluster | Cluster |
| clusterer | Clusterer |
| cluster_cmeans | Fuzzy c-means |
| cluster_dbscan | DBSCAN |
| cluster_gmm | Gaussian mixture model clustering (GMM) |
| cluster_hclust | Hierarchical clustering |
| cluster_kmeans | k-means |
| cluster_louvain_graph | Louvain community detection |
| cluster_pam | PAM (Partitioning Around Medoids) |
| clu_tune | Clustering tuning (intrinsic metric) |
-- D --
| dal_base | Class dal_base |
| dal_graphics | Graphics utilities |
| dal_learner | DAL Learner (base class) |
| dal_transform | DAL Transform |
| dal_tune | DAL Tune (base for hyperparameter search) |
| data_sample | Data sampling abstractions |
| discover | Discover |
| dt_pca | PCA |
-- E --
| evaluate | Evaluate |
-- F --
| feature_generation | Feature generation |
| feature_selection_corr | Feature selection by correlation |
| fit | Fit |
| fit.cla_tune | tune hyperparameters of ml model |
| fit.cluster_dbscan | fit dbscan model |
| fit_curvature_max | Maximum curvature analysis (elbow detection) |
| fit_curvature_min | Minimum curvature analysis (elbow detection) |
-- H --
| hierarchy_cut | Hierarchy mapping by cut |
-- I --
| imputation_simple | Simple imputation |
| inverse_transform | Inverse Transform |
-- K --
| k_fold | K-fold sampling |
-- M --
| minmax | Min-max normalization |
-- N --
| na_removal | Missing value removal |
-- O --
| outliers_boxplot | Outlier removal by boxplot (IQR rule) |
| outliers_gaussian | Outlier removal by Gaussian 3-sigma rule |
-- P --
| pattern_miner | Pattern miner |
| pat_apriori | Apriori rules |
| pat_cspade | cSPADE sequences |
| pat_eclat | ECLAT itemsets |
| plot_bar | Plot bar graph |
| plot_boxplot | Plot boxplot |
| plot_boxplot_class | Boxplot per class |
| plot_correlation | Plot correlation |
| plot_dendrogram | Plot dendrogram |
| 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_pair | Plot scatter matrix |
| plot_pair_adv | Plot advanced scatter matrix |
| plot_parallel | Plot parallel coordinates |
| plot_pieplot | Plot pie |
| plot_pixel | Plot pixel visualization |
| plot_points | Plot points |
| plot_radar | Plot radar |
| plot_scatter | Scatter graph |
| plot_series | Plot series |
| plot_stackedbar | Plot stacked bar |
| plot_ts | Plot time series chart |
| plot_ts_pred | Plot time series with predictions |
| predictor | Predictor (base for classification/regression) |
-- R --
| regression | Regression base class |
| reg_dtree | Decision Tree for regression |
| reg_knn | K-Nearest Neighbors (KNN) Regression |
| reg_lm | Linear regression (lm) |
| reg_mlp | MLP for regression |
| reg_rf | Random Forest for regression |
| reg_svm | SVM for regression |
| reg_tune | Regression tuning (k-fold CV) |
-- S --
| sample_balance | Class balancing (up/down sampling) |
| sample_cluster | Cluster sampling |
| sample_random | Random sampling |
| sample_simple | Simple sampling |
| sample_stratified | Stratified sampling |
| select_hyper | Selection of hyperparameters |
| select_hyper.cla_tune | selection of hyperparameters |
| set_params | Assign parameters |
| set_params.default | Default Assign parameters |
| smoothing | Smoothing (binning/quantization) |
| smoothing_cluster | Smoothing by clustering (k-means) |
| smoothing_freq | Smoothing by equal frequency |
| smoothing_inter | Smoothing by equal interval |
-- T --
| train_test | Train-Test Partition |
| train_test_from_folds | k-fold training and test partition object |
| transform | Transform |
-- Z --
| zscore | Z-score normalization |