55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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"""
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=============================
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Recursive feature elimination
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=============================
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This example demonstrates how Recursive Feature Elimination
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(:class:`~sklearn.feature_selection.RFE`) can be used to determine the
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importance of individual pixels for classifying handwritten digits.
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:class:`~sklearn.feature_selection.RFE` recursively removes the least
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significant features, assigning ranks based on their importance, where higher
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`ranking_` values denote lower importance. The ranking is visualized using both
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shades of blue and pixel annotations for clarity. As expected, pixels positioned
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at the center of the image tend to be more predictive than those near the edges.
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.. note::
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See also :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`
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""" # noqa: E501
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import matplotlib.pyplot as plt
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from sklearn.datasets import load_digits
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from sklearn.feature_selection import RFE
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from sklearn.linear_model import LogisticRegression
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import MinMaxScaler
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# Load the digits dataset
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digits = load_digits()
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X = digits.images.reshape((len(digits.images), -1))
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y = digits.target
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pipe = Pipeline(
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[
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("scaler", MinMaxScaler()),
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("rfe", RFE(estimator=LogisticRegression(), n_features_to_select=1, step=1)),
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]
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)
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pipe.fit(X, y)
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ranking = pipe.named_steps["rfe"].ranking_.reshape(digits.images[0].shape)
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# Plot pixel ranking
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plt.matshow(ranking, cmap=plt.cm.Blues)
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# Add annotations for pixel numbers
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for i in range(ranking.shape[0]):
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for j in range(ranking.shape[1]):
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plt.text(j, i, str(ranking[i, j]), ha="center", va="center", color="black")
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plt.colorbar()
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plt.title("Ranking of pixels with RFE\n(Logistic Regression)")
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plt.show()
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