Sklearn 标准化数据 from __future__ import print_function from sklearn import preprocessing import numpy as np from sklearn.model_selection import train_test_split from sklearn.datasets.samples_generator import make_classification from sklearn.svm import S
首先,弄清楚三个相似但是不同的任务: feature extraction and feature engineering: 将原始数据转换为特征,以适合建模. feature transformation: 对数据的转换以提高算法的精度. feature selection: 删除不必要的特征. 1 Feature Extraction 1.1 Text 1.1.1 Bag of Words 最简单的方法是 Bag of Words,首先有一个词典包含了文本中出现的所有的词,每个句子文本的表示
RESCALING attribute data to values to scale the range in [0, 1] or [−1, 1] is useful for the optimization algorithms, such as gradient descent, that are used within machine learning algorithms that weight inputs (e.g. regression and neural networks).