Leveraging Experiment Lines to Data Analytics

Documentation for package ‘daltoolbox’ version 1.1.747

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-- 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_adv_e	Adversarial Autoencoder - Encode
autoenc_adv_ed	Adversarial Autoencoder - Encode
autoenc_conv_e	Convolutional Autoencoder - Encode
autoenc_conv_ed	Convolutional Autoencoder - Encode
autoenc_denoise_e	Denoising Autoencoder - Encode
autoenc_denoise_ed	Denoising Autoencoder - Encode
autoenc_e	Autoencoder - Encode
autoenc_ed	Autoencoder - Encode-decode
autoenc_lstm_e	LSTM Autoencoder - Encode
autoenc_lstm_ed	LSTM Autoencoder - Decode
autoenc_stacked_e	Stacked Autoencoder - Encode
autoenc_stacked_ed	Stacked Autoencoder - Encode
autoenc_variational_e	Variational Autoencoder - Encode
autoenc_variational_ed	Variational Autoencoder - Encode

-- B --

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 --

-- E --

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

-- K --

K-fold sampling

-- M --

minmax	Min-max normalization
MSE.ts	MSE

-- O --

Outliers

-- P --

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

-- 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	Stratified Random Sampling
select_hyper	Selection hyper parameters
select_hyper.cla_tune	selection of hyperparameters
select_hyper.ts_tune	Select Optimal Hyperparameters for Time Series Models
set_params	Assign parameters
set_params.default	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

-- T --

train_test	Train-Test Partition
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	Extract the First Observations from a 'ts_data' Object
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 --

Z-score normalization

-- misc --

Subset Extraction for Time Series Data