数据预处理时进行特征值的放缩，应该在训练集合测试集上进行相同的放缩，换言之放缩的标准都应该是在测试集上学习到的。
下面展示最大最下放缩的效果：
from matplotlib.pyplot import as plt from sklearn.datasets import make_blobs from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split # 构造数据
X, _ = make_blobs(n_samples=60, centers=5, random_state=7, cluster_std=2 ) X_train, X_test = train_test_split(X, random_state=9, test_size=0.1 ) fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(13, 4) ) # 绘制未经放缩的数据的训练集和测试集
axes[0].scatter(X_train[:, 0], X_train[:, 1], c='b', label='Training set', s=60 ) axes[0].scatter(X_test[:, 0], X_test[:, 1], marker='^', c='r', label='Test set', s=60 ) axes[0].legend(loc=1) axes[0].set_title('Original Data') # 利用 MinMaxScaler 放缩数据
scaler = MinMaxScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) # 可视化正确放缩的数据
axes[1].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1], c='b', label='Training set', s=60 ) axes[1].scatter(X_test_scaled[:, 0], X_test_scaled[:, 1], marker='^', c='r', label='Test set', s=60 ) axes[1].set_title('Scaled Data') # 对测试集进行单独放缩
test_scaler = MinMaxScaler() test_scaler.fit(X_test) X_test_scaled_badly = test_scaler.transform(X_test) # 可视化错误放缩的数据
axes[2].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1], c='b', label='Training set', s=60 ) axes[2].scatter(X_test_scaled_badly[:, 0], X_test_scaled_badly[:, 1], marker='^', c='r', label='Test set', s=60 ) axes[2].set_title('Improperly Scaled Data') # 为每幅图添加坐标轴标题
for ax in axes: ax.set_xlabel('Feature 0') ax.set_ylabel('Feature 1') plt.show()
前两张图看起来一样，但是坐标刻度发生了变化，这便是特征放缩的效果。第三张图是对测试集的放缩是错误的，人为改变了数据的排列。