41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
"""
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==========================
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Plotting Validation Curves
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==========================
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In this plot you can see the training scores and validation scores of an SVM
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for different values of the kernel parameter gamma. For very low values of
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gamma, you can see that both the training score and the validation score are
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low. This is called underfitting. Medium values of gamma will result in high
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values for both scores, i.e. the classifier is performing fairly well. If gamma
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is too high, the classifier will overfit, which means that the training score
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is good but the validation score is poor.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import load_digits
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from sklearn.model_selection import ValidationCurveDisplay
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from sklearn.svm import SVC
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X, y = load_digits(return_X_y=True)
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subset_mask = np.isin(y, [1, 2]) # binary classification: 1 vs 2
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X, y = X[subset_mask], y[subset_mask]
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disp = ValidationCurveDisplay.from_estimator(
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SVC(),
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X,
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y,
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param_name="gamma",
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param_range=np.logspace(-6, -1, 5),
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score_type="both",
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n_jobs=2,
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score_name="Accuracy",
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)
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disp.ax_.set_title("Validation Curve for SVM with an RBF kernel")
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disp.ax_.set_xlabel(r"gamma (inverse radius of the RBF kernel)")
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disp.ax_.set_ylim(0.0, 1.1)
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plt.show()
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