python-scikit-learnScaling data using StandardScaler
from sklearn import datasets, preprocessing
data = datasets.load_diabetes(scaled=False).data
scl = preprocessing.StandardScaler()
scl.fit(data)
data_scaled = scl.transform(data)ctrl + cfrom sklearn importimport module from scikit-learn | datasets.load_diabetesloads sample diabetes database |
.StandardScaler(Scales each feature than values are distributed in | scl.fit(fit scaler |
scl.transform(scale data using trained scaler | |
Usage example
from sklearn import datasets, preprocessing
data = datasets.load_diabetes(scaled=False).data
print(data[0])
scl = preprocessing.StandardScaler()
scl.fit(data)
data_scaled = scl.transform(data)
print(data_scaled[0])output
[ 59. 2. 32.1 101. 157. 93.2 38. 4.
4.8598 87. ]
[ 0.80050009 1.06548848 1.29708846 0.45984057 -0.92974581 -0.73206462
-0.91245053 -0.05449919 0.41853093 -0.37098854]
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