Machine learning regression off-the-shelf
Project description
Machine learning regression (mlregression)
Machine Learning Regression (mlregrresion) is an off-the-shelf implementation fitting and tuning the most popular ML methods (provided by scikit-learn)
Additionally, please contact the authors below if you find any bugs or have any suggestions for improvement. Thank you!
Author: Nicolaj Søndergaard Mühlbach (n.muhlbach at gmail dot com, muhlbach at mit dot edu)
Code dependencies
This code has the following dependencies:
- Python 3.6+
- numpy 1.19+
- pandas 1.3+
- scikit-learn 1+
Usage
# Import
from sklearn.datasets import make_regression
from mlregression.base.base_mlreg import BaseMLRegressor
# Specify estimator
estimator = "RandomForestRegressor"
# Generate data
X, y = make_regression(n_samples=500,
n_features=10,
n_informative=5,
n_targets=1,
bias=0.0,
coef=False,
random_state=1991)
# Instantiate model
mlreg = BaseMLRegressor(estimator=estimator,
max_n_models=2)
# Fit
mlreg.fit(X=X, y=y)
# Access all the usual attributes
mlreg.best_score_
mlreg.best_estimator_
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