python - 如何在同一张图上绘制不同模型的多个学习曲线？

Question

我训练过的模型很少，并且想在单个图表上绘制每个模型的学习曲线

我尝试过这个，并且成功了。但感觉多余。

train_sizes, train_scores, test_scores = learning_curve(model, 
                                                        train_dummies, 
                                                        y,
                                                        cv=5,
                                                      scoring='neg_mean_squared_error')

因为我需要为每个模型重复train_scores和test_scores。

我尝试使用for循环。

首先，我将模型保存在数组中。

arr = [m1,m2,m3]

但是当我启动 for 循环时，它只在图表上生成了一条线。

for i in arr:
  train_sizes, train_scores, test_scores = learning_curve(i, 
                                                    train_dummies, 
                                                    y,
                                                    cv=5,
                                              scoring='neg_mean_squared_error')
  train_mean = np.mean(train_scores, axis=1)
  train_std = np.std(train_scores, axis=1)

  test_mean = np.mean(test_scores, axis=1)
  test_std = np.std(test_scores, axis=1)


  plt.plot(train_sizes, test_mean, label="Cross-validation score")

这是所需的输出

有人会告诉我我缺少什么吗？非常感谢您的宝贵时间。

Answer 1

我看不出你正在做的事情有什么问题。这对我有用(部分摘自 here ):

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve

digits = load_digits()
X, y = digits.data, digits.target
for i in [GaussianNB(), SVC(gamma=0.001)]:
    (train_sizes,
     train_scores,
     test_scores) = learning_curve(i, X, y, cv=5)
    test_mean = np.mean(test_scores, axis=1)
    plt.plot(train_sizes, test_mean, label="Cross-validation score")

plt.legend()
plt.show()