daltoolbox1.3.717 package

Action implementation for transform

Action

Adjust categorical mapping

Adjust to data frame

Adjust factors

Adjust to matrix

Aggregation by groups

Autoencoder base (encoder)

Autoencoder base (encoder + decoder)

Categorical mapping (one‑hot encoding)

Bagging (ipred)

Boosting (adabag)

Decision Tree for classification

Logistic regression (GLM)

LASSO logistic regression (glmnet)

K-Nearest Neighbors (KNN) Classification

Majority baseline classifier

MLP for classification

Multinomial logistic regression

Naive Bayes Classifier

Random Forest for classification

CART (rpart)

SVM for classification

Classification tuning (k-fold CV)

XGBoost

Classification base class

Clustering tuning (intrinsic metric)

Fuzzy c-means

DBSCAN

Gaussian mixture model clustering (GMM)

Hierarchical clustering

k-means

Louvain community detection

PAM (Partitioning Around Medoids)

Cluster

Clusterer

Class dal_base

Graphics utilities

DAL Learner (base class)

DAL Transform

DAL Tune (base for hyperparameter search)

Data sampling abstractions

Discover

PCA

Evaluate

Feature generation

Feature selection by correlation

Maximum curvature analysis (elbow detection)

Minimum curvature analysis (elbow detection)

tune hyperparameters of ml model

fit dbscan model

Fit

Hierarchy mapping by cut

Simple imputation

Inverse Transform

K-fold sampling

Min-max normalization

Missing value removal

Outlier removal by boxplot (IQR rule)

Outlier removal by Gaussian 3-sigma rule

Apriori rules

cSPADE sequences

ECLAT itemsets

Pattern miner

Plot bar graph

Boxplot per class

Plot boxplot

Plot correlation

Plot dendrogram

Plot density per class

Plot density

Plot grouped bar

Plot histogram

Plot lollipop

Plot advanced scatter matrix

Plot scatter matrix

Plot parallel coordinates

Plot pie

Plot pixel visualization

Plot points

Plot radar

Scatter graph

Plot series

Plot stacked bar

Plot time series with predictions

Plot time series chart

Predictor (base for classification/regression)

Decision Tree for regression

K-Nearest Neighbors (KNN) Regression

Linear regression (lm)

MLP for regression

Random Forest for regression

SVM for regression

Regression tuning (k-fold CV)

Regression base class

Class balancing (up/down sampling)

Cluster sampling

Random sampling

Simple sampling

Stratified sampling

selection of hyperparameters

Selection of hyperparameters

Default Assign parameters

Assign parameters

Smoothing by clustering (k-means)

Smoothing by equal frequency

Smoothing by equal interval

Smoothing (binning/quantization)

k-fold training and test partition object

Train-Test Partition

Transform

Z-score normalization