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

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

Hunga-Bunga

HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!

Lets brute force all sklearn models with all of sklearn parameters! Ahhh Hunga Bunga!!
from hunga_bunga import HungaBungaClassifier, HungaBungaRegressor
And then simply:

Hunga-Bunga


What?

Yes.

No! Really! What?

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.

HOW IT WORKS

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

MODELS

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.

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)

Option I (Recommended): brain = False

As any other sklearn model

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

And import from here

from hunga_bunga import HungaBungaClassifier, HungaBungaRegressor

Option II: brain = True

As any other sklearn model

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

The output looks this:

Model accuracy Time/clf (s)
SGDClassifier 0.967 0.001
LogisticRegression 0.940 0.001
Perceptron 0.900 0.001
PassiveAggressiveClassifier 0.967 0.001
MLPClassifier 0.827 0.018
KMeans 0.580 0.010
KNeighborsClassifier 0.960 0.000
NearestCentroid 0.933 0.000
RadiusNeighborsClassifier 0.927 0.000
SVC 0.960 0.000
NuSVC 0.980 0.001
LinearSVC 0.940 0.005
RandomForestClassifier 0.980 0.015
DecisionTreeClassifier 0.960 0.000
ExtraTreesClassifier 0.993 0.002

The winner is: ExtraTreesClassifier with score 0.993.

GitHub