Leveraging Experiment Lines to Data Analytics

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Documentation for package ‘daltoolbox’ version 1.2.727

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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
autoenc_base_e Autoencoder - Encode
autoenc_base_ed Autoencoder - Encode-decode
Boston Boston Housing Data (Regression)
categ_mapping Categorical mapping
classification classification
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
cla_svm SVM for classification
cla_tune Classification Tune
cluster Cluster
clusterer Clusterer
cluster_dbscan DBSCAN
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
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
outliers_boxplot outliers_boxplot
outliers_gaussian outliers_gaussian
plot_bar Plot bar graph
plot_boxplot Plot boxplot
plot_boxplot_class 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 time series chart
plot_ts_pred Plot a time series chart with predictions
predictor DAL Predict
regression Regression
reg_dtree Decision Tree for regression
reg_knn knn regression
reg_mlp MLP for regression
reg_rf Random Forest for regression
reg_svm SVM for regression
reg_tune Regression Tune
sample_random Sample Random
sample_stratified Stratified Random Sampling
select_hyper Selection hyper parameters
select_hyper.cla_tune selection of hyperparameters
set_params Assign parameters
set_params.default Default Assign parameters
smoothing Smoothing
smoothing_cluster Smoothing by cluster
smoothing_freq Smoothing by Freq
smoothing_inter Smoothing by interval
train_test Train-Test Partition
train_test_from_folds k-fold training and test partition object
transform Transform
zscore Z-score normalization