稀疏性示例：仅拟合特征 1 和 2#

下面拟合并绘制了糖尿病数据集的特征 1 和 2。它说明了虽然特征 2 在完整模型中具有很强的系数，但与仅特征 1 相比，它并没有给我们太多关于 y 的信息。

# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause

首先，我们加载糖尿病数据集。

import numpy as np

from sklearn import datasets

X, y = datasets.load_diabetes(return_X_y=True)
indices = (0, 1)

X_train = X[:-20, indices]
X_test = X[-20:, indices]
y_train = y[:-20]
y_test = y[-20:]

接下来，我们拟合一个线性回归模型。

from sklearn import linear_model

ols = linear_model.LinearRegression()
_ = ols.fit(X_train, y_train)

最后，我们从三个不同的视图绘制图形。

import matplotlib.pyplot as plt

# unused but required import for doing 3d projections with matplotlib < 3.2
import mpl_toolkits.mplot3d  # noqa: F401


def plot_figs(fig_num, elev, azim, X_train, clf):
    fig = plt.figure(fig_num, figsize=(4, 3))
    plt.clf()
    ax = fig.add_subplot(111, projection="3d", elev=elev, azim=azim)

    ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c="k", marker="+")
    ax.plot_surface(
        np.array([[-0.1, -0.1], [0.15, 0.15]]),
        np.array([[-0.1, 0.15], [-0.1, 0.15]]),
        clf.predict(
            np.array([[-0.1, -0.1, 0.15, 0.15], [-0.1, 0.15, -0.1, 0.15]]).T
        ).reshape((2, 2)),
        alpha=0.5,
    )
    ax.set_xlabel("X_1")
    ax.set_ylabel("X_2")
    ax.set_zlabel("Y")
    ax.xaxis.set_ticklabels([])
    ax.yaxis.set_ticklabels([])
    ax.zaxis.set_ticklabels([])


# Generate the three different figures from different views
elev = 43.5
azim = -110
plot_figs(1, elev, azim, X_train, ols)

elev = -0.5
azim = 0
plot_figs(2, elev, azim, X_train, ols)

elev = -0.5
azim = 90
plot_figs(3, elev, azim, X_train, ols)

plt.show()

脚本总运行时间：（0 分 0.198 秒）