Python機器學習筆記(十一)：機器學習的資料前處理技術
2 min readApr 10, 2019
Rescale data
Standardize data
Normalize data
Binarize data
1. Rescale Data
#將資料比例縮放到0與1之間# Rescale data (between 0 and 1)
import pandas
import scipy
import numpy
from sklearn.preprocessing import MinMaxScaler
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX = scaler.fit_transform(X)
# summarize transformed data
numpy.set_printoptions(precision=3)
print(rescaledX[0:5,:])
2. Standardize Data
#將資料常態分布化，平均值會變為0, 標準差變為1，使離群值影響降低
#MinMaxScaler與StandardScaler類似from sklearn.preprocessing import StandardScaler
import pandas
import numpy
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
scaler = StandardScaler().fit(X)
rescaledX = scaler.transform(X)
# summarize transformed data…