🚙Praktikum 3

stackking

Stacking

Lengkapi bagian berikut dengan data sesuai tugas, dan tentukan perbedaan nilai akurasi antara Random Forest, Adaboost, dan Stacking

from sklearn.ensemble import RandomForestClassifier, StackingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier



layer_one_estimators = [
                        ('rf_1', RandomForestClassifier(n_estimators=10, random_state=42)),
                        ('knn_1', KNeighborsClassifier(n_neighbors=5))             
                       ]
layer_two_estimators = [
                        ('dt_2', DecisionTreeClassifier()),
                        ('rf_2', RandomForestClassifier(n_estimators=50, random_state=42)),
                       ]
layer_two = StackingClassifier(estimators=layer_two_estimators, final_estimator=LogisticRegression())


clf = StackingClassifier(estimators=layer_one_estimators, final_estimator=layer_two)

X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
clf.fit(X_train, y_train).score(X_test, y_test)