[英]How to plot multiple learning curve from different model on the same graph?
我训练的模型很少,并且想在单个图上绘制每个模型的学习曲线
我试过这个,并且成功了。 但感觉是多余的。
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,我将模型保存在一个数组中。
arr = [m1,m2,m3]
但是当我开始for循环时,它只在图表上生成了一行。
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? 非常感谢您的时间。
我看不出你在做什么。这对我有用(部分取自这里):
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()
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.