54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
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"""
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=========================================================
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Logistic Regression 3-class Classifier
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=========================================================
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Show below is a logistic-regression classifiers decision boundaries on the
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first two dimensions (sepal length and width) of the `iris
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<https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The datapoints
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are colored according to their labels.
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"""
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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from sklearn import datasets
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.linear_model import LogisticRegression
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features.
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Y = iris.target
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# Create an instance of Logistic Regression Classifier and fit the data.
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logreg = LogisticRegression(C=1e5)
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logreg.fit(X, Y)
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_, ax = plt.subplots(figsize=(4, 3))
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DecisionBoundaryDisplay.from_estimator(
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logreg,
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X,
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cmap=plt.cm.Paired,
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ax=ax,
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response_method="predict",
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plot_method="pcolormesh",
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shading="auto",
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xlabel="Sepal length",
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ylabel="Sepal width",
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eps=0.5,
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)
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)
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plt.xticks(())
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plt.yticks(())
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plt.show()
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