Note

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递归特征消除#

本示例演示了如何使用递归特征消除（RFE ）来确定单个像素在手写数字分类中的重要性。RFE 递归地移除最不重要的特征，根据其重要性分配排名，其中较高的 ranking_ 值表示较低的重要性。排名通过蓝色阴影和像素注释进行可视化，以提高清晰度。正如预期的那样，位于图像中心的像素比边缘的像素更具预测性。

Note

另请参见带交叉验证的递归特征消除

Ranking of pixels with RFE (Logistic Regression)

import matplotlib.pyplot as plt

from sklearn.datasets import load_digits
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler

# 加载数字数据集
digits = load_digits()
X = digits.images.reshape((len(digits.images), -1))
y = digits.target

pipe = Pipeline(
    [
        ("scaler", MinMaxScaler()),
        ("rfe", RFE(estimator=LogisticRegression(), n_features_to_select=1, step=1)),
    ]
)

pipe.fit(X, y)
ranking = pipe.named_steps["rfe"].ranking_.reshape(digits.images[0].shape)

# 绘制像素排名
plt.matshow(ranking, cmap=plt.cm.Blues)

# 添加像素编号的注释
for i in range(ranking.shape[0]):
    for j in range(ranking.shape[1]):
        plt.text(j, i, str(ranking[i, j]), ha="center", va="center", color="black")

plt.colorbar()
plt.title("Ranking of pixels with RFE\n(Logistic Regression)")
plt.show()