Decision Tree

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
X,y=load_breast_cancer(return_X_y=True); Xtr,Xte,ytr,yte=train_test_split(X,y,random_state=1)
for n,m in {"LogReg":make_pipeline(StandardScaler(),LogisticRegression(max_iter=500)),"KNN":make_pipeline(StandardScaler(),KNeighborsClassifier()),
 "RandomForest":RandomForestClassifier(random_state=0),"GradBoost":GradientBoostingClassifier()}.items():
    print(f"{n:13s} accuracy = {m.fit(Xtr,ytr).score(Xte,yte):.3f}")