Leveraging Experiment Lines to Data Analytics

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Documentation for package ‘daltoolbox’ version 1.0.767

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A B C D E F I K M O P R S T Z misc

-- 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
adjust_ts_data adjust 'ts_data'
autoenc_encode Autoencoder - Encode
autoenc_encode_decode Autoencoder - Encode

-- B --

Boston Boston Housing Data (Regression)

-- C --

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

-- D --

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

-- E --

evaluate evaluate

-- F --

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

-- I --

inverse_transform Inverse Transform

-- K --

k_fold k-fold sampling

-- M --

minmax min-max normalization

-- O --

outliers Outliers

-- P --

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

-- R --

R2.ts R2
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

-- S --

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
smoothing Smoothing
smoothing_cluster Smoothing by cluster
smoothing_freq Smoothing by Freq
smoothing_inter Smoothing by interval

-- T --

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 Average - EMA)
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

-- Z --

zscore z-score normalization

-- misc --

[.ts_data Extract a subset of a time series stored in an object