estimate classifier

Ensemble_learning/ensemble_learning.py

@@ -0,0 +1,36 @@
+#%%
+from sklearn.datasets import make_moons
+
+X,Y = make_moons(n_samples=10000)
+print(X)
+print(Y)
+
+#%%
+from sklearn.model_selection import train_test_split
+
+X_train,X_test,Y_train,Y_test= train_test_split(
+    X,Y,test_size=0.2,random_state=42)
+print(X_train)
+print(Y_train)
+
+#%%
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.ensemble import VotingClassifier
+
+log_clf = LogisticRegression()
+rand_clf = RandomForestClassifier()
+svc_clf = SVC()
+
+vote_clf = VotingClassifier(estimators=[
+    ('lc',log_clf),('rc',rand_clf),('sc',svc_clf)],voting='hard')
+
+vote_clf.fit(X_train,Y_train)
+
+#%%
+from sklearn.metrics import accuracy_score
+for clf in (log_clf, rand_clf, svc_clf, vote_clf):   
+    clf.fit(X_train, Y_train)
+    Y_pred = clf.predict(X_test)
+    print(clf.__class__.__name__, accuracy_score(Y_test, Y_pred))