Python-Based Extensions for Data Analytics Workflows
Adversarial Autoencoder - Encode
Adversarial Autoencoder - Encode-Decode
Convolutional Autoencoder - Encode
Convolutional Autoencoder - Encode-Decode
Denoising Autoencoder - Encode
Denoising Autoencoder - Encode-Decode
Autoencoder - Encode
Autoencoder - Encode-Decode
LSTM Autoencoder - Encode
LSTM Autoencoder - Encode-Decode
Stacked Autoencoder - Encode
Stacked Autoencoder - Encode-Decode
Variational Autoencoder - Encode
Variational Autoencoder - Encode-Decode
Oversampling
Subsampling
Forward Stepwise Selection
Information Gain
LASSO Feature Selection
Relief
Feature Selection
Gradient Boosting Classifier
K-Nearest Neighbors Classifier
Multi-layer Perceptron Classifier
Gaussian Naive Bayes Classifier
Random Forest Classifier
Support Vector Machine Classification
Conv1D
LSTM
Provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. Includes tools for data sampling, transformation, feature selection, balancing strategies (e.g., SMOTE), and model construction. These capabilities leverage Python libraries via the reticulate interface, enabling seamless integration with a broader machine learning ecosystem. Supports instance selection and hybrid workflows that combine R and Python functionalities for flexible and reproducible analytical pipelines. The architecture is inspired by the Experiment Lines approach, which promotes modularity, extensibility, and interoperability across tools. More information on Experiment Lines is available in Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
Useful links