better-regressions 0.1.1

Advanced regression methods with sklearn-like interface

Better Regressions

Advanced regression methods with an sklearn-like interface.

Current Features

• Linear:
  • Configurable regularization: Ridge with given alpha / BayesianRidge / ARD
  • "Better bias" option to properly regularize the intercept term
• Scaler:
  • Configurable preprocessing: Standard scaling (by second moment) / Quantile transformation with uniform/normal output / Power transformation
  • AutoScaler to automatically select the best scaling method based on validation split
• Smooth: Boosting-based regression using smooth functions for features
  • SuperSmoother: Adaptive-span smoother for arbitrary complex functions.
  • Angle: Bagging of piecewise-linear functions, it's less flexible but because of that it's more robust to overfitting.

Installation

pip install better-regressions

Basic Usage

from better_regressions import auto_angle, auto_linear, Linear, Scaler
from sklearn.datasets import make_regression
import numpy as np

X, y = make_regression(n_samples=100, n_features=5, noise=0.1)
model = auto_angle(n_breakpoints=2)
model.fit(X, y)
y_pred = model.predict(X)
print(repr(model))