sklearn.model_selection.learning_curve(estimator, X, y, *, groups=None, train_sizes=array([0.1, 0.33, 0.55, 0.78, 1. ]), 
cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch='
 
all', verbose=0, shuffle=False, random_state=None, error_score=nan, return_times=False)

主要說一下：train_sizes

train_sizes:array-like, shape (n_ticks,), dtype float or int

訓練樣本的相對的或絕對的數字，這些量的樣本將會生成learning curve。如果dtype是float，他將會被視為最大數量訓練集的一部分（這個由所選擇的驗證方法所決定）。否則，他將會被視為訓練集的絕對尺寸。要注意的是，對於分類而言，樣本的大小必須要充分大，達到對於每一個分類都至少包含一個樣本的情況

train_sizes_abs：array, shape = (n_unique_ticks,), dtype int：用於生成learning curve的訓練集的樣本數。由於重複的輸入將會被刪除，所以ticks可能會少於n_ticks.

train_scores: array, shape (n_ticks, n_cv_folds)：在訓練集上的分數

test_scores: array, shape (n_ticks, n_cv_folds)：在測試集上的分數

使用鳶尾花作為例子

import pandas as pd 
import matplotlib.pyplot as plt 
 
import numpy as np 
from sklearn.datasets import load_iris
from sklearn.model_selection import learning_curve
from sklearn.linear_model import LogisticRegression # 用於模型預測

iris=load_iris()
x=iris.data
y=iris.target

train_sizes, train_scores, test_scores =\
                learning_curve(estimator= LogisticRegression(random_state=1),
                               X=x,
                               y=y,
                               train_sizes=np.linspace(0.5, 1.0, 5), #在0.1和1間線性的取10個值
                               cv=10,
                               n_jobs=1)

train_sizes, train_scores, test_scores
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, train_mean,
         color='blue', marker='o',
         markersize=5, label='training accuracy')
plt.fill_between(train_sizes,
                 train_mean + train_std,
                 train_mean - train_std,
                 alpha=0.15, color='blue')
plt.plot(train_sizes, test_mean,
         color='green', linestyle='--',
         marker='s', markersize=5,
         label='validation accuracy')
plt.fill_between(train_sizes,
                 test_mean + test_std,
                 test_mean - test_std,
                 alpha=0.15, color='green')
plt.grid()
plt.xlabel('Number of training samples')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.ylim([0.6, 1.0])
plt.tight_layout()
plt.show()