Standard Scaling

import sklearn
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_breast_cancer

cancer=load_breast_cancer()

cancer_df = pd.DataFrame(data=cancer.data, columns=cancer.feature_names)
cancer_df['target'] = cancer.target
cancer_df.head()


from sklearn.preprocessing import StandardScaler

std = StandardScaler()
std.fit(X_train)
X_train_scaled = std.transform(X_train)
X_test_scaled = std.transform(X_test)
dtc.fit(X_train_scaled, y_train)
print('모델의 정확도 :', round(dtc.score(X_test_scaled, y_test), 4))

모든 피처들을 평균이 0, 분산이 1인 정규분포를 갖도록 만들어 표준화한다.