59 lines
1.4 KiB
Python
59 lines
1.4 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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Linear Regression Variance Example
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=========================================================
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Due to the few points in each dimension and the straight
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line that linear regression uses follows these points
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as well as it can, noise on the observations will cause
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great variace as shown in this plot. Every line's slope
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can vary quite a bit for each prediction due to the noise
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induced in the observations.
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"""
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print __doc__
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# Code source: Gael Varoqueux
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# Modified for Documentation merge by Jaques Grobler
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# License: BSD
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import numpy as np
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import pylab as pl
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from sklearn import linear_model
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X_train = np.c_[ .5, 1].T
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y_train = [.5, 1]
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X_test = np.c_[ 0, 2].T
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fig = pl.figure(1, figsize=(4, 3))
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pl.clf()
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ax = pl.axes([.12, .12, .8, .8])
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np.random.seed(0)
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ols = linear_model.LinearRegression()
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for _ in range(6):
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this_X = .1*np.random.normal(size=(2, 1)) + X_train
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ols.fit(this_X, y_train)
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ax.plot(X_test, ols.predict(X_test), color='.5')
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ax.scatter(this_X, y_train, s=3, c='.5', marker='o', zorder=10)
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ols.fit(X_train, y_train)
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ax.plot(X_test, ols.predict(X_test), linewidth=2, color='blue')
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ax.scatter(X_train, y_train, s=30, c='r', marker='+', zorder=10)
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ax.set_xticks(())
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ax.set_yticks(())
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ax.set_xlabel('X')
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ax.set_ylabel('y')
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ax.set_xlim(0, 2)
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pl.show()
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