Sklearn-绘图分类报告提供的输出与基本平均值不同？

问题描述

我想利用此答案，解析报表没有麻烦。您可以直接使用分类报告的输出读取为 pd.DataFrame 。然后，您可以使用选项以渲染热图。

With the advent of output_dict param in classification_report, there is no hassle for parsing the report. You can directly use the output of classification report to be read as pd.DataFrame. Then, you could use the pd.Style option to render the heat map.

示例：

from sklearn.metrics import classification_report
import numpy as np
import pandas as pd

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV


X, y = make_classification(n_samples=1000, n_features=30,
                           n_informative=12,
                           n_clusters_per_class=1, n_classes=10,
                           class_sep=2.0, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, stratify=y)


clf = LogisticRegression(max_iter=1000, random_state=42).fit(X_train, y_train)



df = pd.DataFrame(classification_report(clf.predict(X_test), 
                                        y_test, digits=2,
                                        output_dict=True)).T

df['support'] = df.support.apply(int)

df.style.background_gradient(cmap='viridis',
                             subset=pd.IndexSlice['0':'9', :'f1-score'])

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