New Release v0.1.6

New Version Release Highlights:

• Addition of New Models: We've expanded our model suite to include the following architectures:

  • FT-Transformer: Leverages transformer encoders for improved performance on tabular data.
  • MLP (Multi-Layer Perceptron): A classical deep learning model for handling a wide range of tabular data tasks.
  • ResNet: Adapted from the classical ResNet architecture and proven to be a good baseline for tabular tasks.
  • TabTransformer: Utilizes transformer-based models for categorical features.

• Bidirectional and Feature Interaction Capabilities: Mambular now includes bidirectional capabilities and enhanced feature interaction mechanisms, enabling more complex and dynamic data representations and improving model accuracy.

• Architectural Restructuring: The internal architecture has been restructured to facilitate the easy integration of new models. This modular approach simplifies the process of extending Mambular with custom models.

• New Preprocessing Methods: We have introduced new preprocessing techniques to better prepare your data for modeling:

  • Quantile Preprocessing: Transforms numerical features to follow a uniform or normal distribution, improving robustness to outliers.
  • Polynomial Features: Generates polynomial and interaction features to capture more complex relationships within the data.
  • Spline Transformation: Applies piecewise polynomial functions to numerical features, effectively capturing nonlinear relationships.