Brute Force all scikit-learn models and all scikit-learn parameters with fit predict.

from hunga_bunga import HungaBungaClassifier, HungaBungaRegressor

Yes.

Many believe that

most of the work of supervised (non-deep) Machine Learning lies in feature engineering, whereas the model-selection process is just running through all the models or just take xgboost.

So here is an automation for that.

Runs through all sklearn models (both classification and regression), with all possible hyperparameters, and rank using cross-validation.

Runs all the model available on sklearn for supervised learning here. The categories are:

• Generalized Linear Models
• Kernel Ridge
• Support Vector Machines
• Nearest Neighbors
• Gaussian Processes
• Naive Bayes
• Trees
• Neural Networks
• Ensemble methods

Note: Some models were dropped out (nearly none of them..) and some crash or cause exceptions from time to time. It takes REALLY long to test this out so clearing exceptions took me a while.

pip install hunga-bunga

Dependencies

- Python (>= 2.7)
- NumPy (>= 1.11.0)
- SciPy (>= 0.17.0)
- joblib (>= 0.11)
- scikit-learn (>=0.20.0)
- tabulate (>=0.8.2)
- tqdm (>=4.28.1)

As any other sklearn model

clf = HungaBungaClassifier()
clf.fit(x, y)
clf.predict(x)

And import from here

from hunga_bunga import HungaBungaClassifier, HungaBungaRegressor

As any other sklearn model

clf = HungaBungaClassifier(brain=True)
clf.fit(x, y)

The output looks this:

The winner is: ExtraTreesClassifier with score 0.993.