拟合学习曲线的纵坐标表示什么_学习曲线(learning curve)来判断模型状态：过拟合欠拟合...

学习曲线(learning curve)来判断模型状态：过拟合欠拟合

发布时间：2018-03-13 21:52,

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学习曲线(learning curve)来判断模型状态：过拟合欠拟合

学习曲线是什么

学习曲线是不同训练集大小，模型在训练集和验证集上的得分变化曲线。也就是以样本数为横坐标，训练和交叉验证集上的得分(如准确率)为纵坐标。learning

curve可以帮助我们判断模型现在所处的状态：过拟合(overfiting / high variance) or 欠拟合(underfitting /

high bias)

模型欠拟合、过拟合、偏差和方差平衡 时对应的学习曲线如下图所示：

怎么看学习曲线

左上角的图中训练集和验证集上的曲线能够收敛。在训练集合验证集上准确率相差不大，却都很差。这说明模拟对已知数据和未知都不能进行准确的预测，属于高偏差。这种情况模型很可能是欠拟合。可以针对欠拟合采取对应的措施。

右上角的图中模型在训练集上和验证集上的准确率差距很大。说明模型能够很好的拟合已知数据，但是泛化能力很差，属于高方差。模拟很可能过拟合，要采取过拟合对应的措施

怎么画学习曲线

官方学习曲线例子

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_digitsfrom sklearn.model_selection import learning_curve from

sklearn.model_selectionimport ShuffleSplit def plot_learning_curve(estimator,

title, X, y, ylim=None, cv=None, n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):

""" 画出data在某模型上的learning curve. 参数解释 ---------- estimator : 你用的分类器。 title :

表格的标题。 X : 输入的feature，numpy类型 y : 输入的target vector ylim : tuple格式的(ymin, ymax),

设定图像中纵坐标的最低点和最高点 cv :

做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份) n_jobs :

并行的的任务数(默认1) """ plt.figure() plt.title(title) if ylim is not None:

plt.ylim(*ylim) plt.xlabel("Training examples") plt.ylabel("Score")

train_sizes, train_scores, test_scores = learning_curve( estimator, X, y,

cv=cv, n_jobs=n_jobs, train_sizes=train_sizes) train_scores_mean =

np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1)

test_scores_mean = np.mean(test_scores, axis=1) test_scores_std =

np.std(test_scores, axis=1) plt.grid() plt.fill_between(train_sizes,

train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,

alpha=0.1, color="r") plt.fill_between(train_sizes, test_scores_mean -

test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g")

plt.plot(train_sizes, train_scores_mean,'o-', color="r", label="Training score"

) plt.plot(train_sizes, test_scores_mean,'o-', color="g", label=

"Cross-validation score") plt.legend(loc="best") plt.draw() plt.show() midpoint

= ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] -

test_scores_std[-1])) / 2 diff = (train_scores_mean[-1] + train_scores_std[-1])

- (test_scores_mean[-1] - test_scores_std[-1]) return midpoint, diff digits =

load_digits() X, y = digits.data, digits.target title ="Learning Curves (Naive

Bayes)" # Cross validation with 100 iterations to get smoother mean test and

train # score curves, each time with 20% data randomly selected as a validation

set. cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0) estimator =

GaussianNB() plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01),

cv=cv, n_jobs=4) title = "Learning Curves (SVM, RBF kernel, $\gamma=0.001$)" #

SVC is more expensive so we do a lower number of CV iterations: cv =

ShuffleSplit(n_splits=10, test_size=0.2, random_state=0) estimator = SVC(gamma=

0.001) plot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv, n_jobs=4)

参考文献

用学习曲线 learning curve 来判别过拟合问题

机器学习系统模型调优实战–所有调优技术都附相应的scikit-learn实现

官方文档