@(131 - Machine Learning | 机器学习)
1 Feature Scaling
transforms features to have range [0,1]
according to the formula
$x' = \frac{x-x_{min}}{x_{max}-x_{min}} $
1.1 Sklearn - MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import numpy
weights = numpy.array([[115.],[140.],[175.]])
#MinMaxScaler assumes floating point values as input
scaler = MinMaxScaler()
rescaled_weight = scaler.fit_transform(weights)
print rescaled_weight
[[0. ]
[0.41666667]
[1. ]]
1.2 Algorithm affected by feature rescaling？
□ 决策树
□ 使用 RBF 核函数的 SVM√
□ 线性回归
□ K-均值聚类√
Decision Trees use vertical and horizontal lines so there is no trade off.
SVM with RBF Kernel requires making trade-offs in dimensions.
In linear regression, the coefficient and the feature always go together.
K-Means Clustering requires making trade-offs in dimensions.
Algorithms in which two dimensions affect the outcome will be affected by rescaling.