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43 lines
1.7 KiB
Markdown
43 lines
1.7 KiB
Markdown
# Examples
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## Notebooks
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/benchmark.ipynb) Benchmark
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/features.ipynb) Some features
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/gridsearch.ipynb) Gridsearch
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/ensemble.ipynb) Ensembles
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## Sample Code
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```python
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import time
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_iris
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from stree import Stree
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random_state = 1
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X, y = load_iris(return_X_y=True)
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Xtrain, Xtest, ytrain, ytest = train_test_split(
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X, y, test_size=0.2, random_state=random_state
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)
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now = time.time()
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print("Predicting with max_features=sqrt(n_features)")
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clf = Stree(random_state=random_state, max_features="auto")
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clf.fit(Xtrain, ytrain)
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print(f"Took {time.time() - now:.2f} seconds to train")
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print(clf)
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print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}")
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print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")
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print("=" * 40)
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print("Predicting with max_features=n_features")
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clf = Stree(random_state=random_state)
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clf.fit(Xtrain, ytrain)
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print(f"Took {time.time() - now:.2f} seconds to train")
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print(clf)
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print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}")
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print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")
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```
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