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e19d10f6a7
* Add first doc info to sources * Update doc to separate classes in api * Refactor build_predictor * Fix random_sate issue in non linear kernels * Refactor score method using base class implementation * Some quality refactoring * Fix codecov config. * Add sigmoid kernel * Refactor setup and add Makefile
1.9 KiB
1.9 KiB
Examples
Notebooks
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Benchmark -
Benchmark -
Some features
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Gridsearch
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Ensembles
Sample Code
import time
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from stree import Stree
random_state = 1
X, y = load_iris(return_X_y=True)
Xtrain, Xtest, ytrain, ytest = train_test_split(
X, y, test_size=0.2, random_state=random_state
)
now = time.time()
print("Predicting with max_features=sqrt(n_features)")
clf = Stree(random_state=random_state, max_features="auto")
clf.fit(Xtrain, ytrain)
print(f"Took {time.time() - now:.2f} seconds to train")
print(clf)
print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}")
print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")
print("=" * 40)
print("Predicting with max_features=n_features")
clf = Stree(random_state=random_state)
clf.fit(Xtrain, ytrain)
print(f"Took {time.time() - now:.2f} seconds to train")
print(clf)
print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}")
print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")