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使用LARS的Lasso路径#

使用LARS算法在糖尿病数据集上计算沿正则化参数的Lasso路径。每种颜色代表系数向量的不同特征，并显示为正则化参数的函数。

Computing regularization path using the LARS ...
.

# 作者：scikit-learn 开发者
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn import datasets, linear_model

X, y = datasets.load_diabetes(return_X_y=True)

print("Computing regularization path using the LARS ...")
_, _, coefs = linear_model.lars_path(X, y, method="lasso", verbose=True)

xx = np.sum(np.abs(coefs.T), axis=1)
xx /= xx[-1]

plt.plot(xx, coefs.T)
ymin, ymax = plt.ylim()
plt.vlines(xx, ymin, ymax, linestyle="dashed")
plt.xlabel("|coef| / max|coef|")
plt.ylabel("Coefficients")
plt.title("LASSO Path")
plt.axis("tight")
plt.show()