在决策边界图中显示评估指标答案

【问题标题】：Show evaluation metrics in decision boundary plot在决策边界图中显示评估指标
【发布时间】：2021-10-10 13:39:31
【问题描述】：

我正在研究不平衡分类。我想在我的决策边界图中添加 g-mean 和准确性。很高兴在情节中看到这些评分指标的差异。我看不到在此决策边界图中计算这些分数的任何选项。有没有办法在我的决策边界图中添加这些额外的信息。我很感激你的时间。谢谢！

import numpy as np
import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from sklearn.metrics import make_scorer
from imblearn.metrics import geometric_mean_score
from mlxtend.plotting import plot_decision_regions
import matplotlib.gridspec as gridspec
import itertools
gmean = make_scorer(geometric_mean_score, greater_is_better=True)
scoring = {'G-mean': gmean, 'Accuracy':'accuracy'}
X, y = make_blobs(n_samples=[1000, 10],centers=[[0.0, 0.0], [2.0, 2.0]],cluster_std= [1.5, 0.5],random_state=0, shuffle=False)
clf1 = LogisticRegression(max_iter=100000)
clf2 = LogisticRegression(class_weight="balanced",max_iter=100000)

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10,8))

labels = ['Logistic Regression', 'Weighted Logistic Regression']
for clf, lab, grd in zip([clf1, clf2],
                         labels,
                         itertools.product([0, 1], repeat=2)):

    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
    plt.title(lab)
plt.show()

【问题讨论】：

标签： matplotlib machine-learning scikit-learn classification logistic-regression

【解决方案1】：

您可以使用plt.text() 在您的决策边界图中添加 g-mean 和准确性。

例如：

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(15, 8))

labels = ['Logistic Regression', 'Weighted Logistic Regression']

for clf, lab, grd in zip([clf1, clf2],
                         labels,
                         itertools.product([0, 1], repeat=2)):

    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    ax.text(6, 4, "gmean : ", fontsize=10)
    ax.text(6, 2, "accuracy : ", fontsize=10)
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
    plt.title(lab)
plt.show()

【讨论】：