数字型特征可以直接作为特征,但是对于一个多维的特征,某一个特征的取值范围特别大,很可能导致其他特征对结果的影响被忽略,这时候我们需要对数字型特征进行预处理,常见的预处理方式有以下几种。
1.标准化:
>>> from sklearn import preprocessing >>> import numpy as np >>> X = np.array([[ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.]]) >>> X_scaled = preprocessing.scale(X) >>> X_scaled array([[ 0. ..., -1.22..., 1.33...], [ 1.22..., 0. ..., -0.26...], [-1.22..., 1.22..., -1.06...]])
2.正则化:
>>> X = [[ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.]] >>> X_normalized = preprocessing.normalize(X, norm='l2') >>> X_normalized array([[ 0.40..., -0.40..., 0.81...], [ 1. ..., 0. ..., 0. ...], [ 0. ..., 0.70..., -0.70...]])
3.归一化:
>>> X_train = np.array([[ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.]]) ... >>> min_max_scaler = preprocessing.MinMaxScaler() >>> X_train_minmax = min_max_scaler.fit_transform(X_train) >>> X_train_minmax array([[ 0.5 , 0. , 1. ], [ 1. , 0.5 , 0.33333333], [ 0. , 1. , 0. ]])