Prediksi churn dengan decision tree

Machine Learning untuk Pemasaran dengan Python

Karolis Urbonas

Head of Analytics & Science, Amazon

Pengenalan decision tree

Aturan Decision Tree pada dataset Kelangsungan Hidup Titanic

Langkah pemodelan

Pisahkan data menjadi train dan test
Inisialisasi model
Latih model pada data train
Prediksi nilai pada data test
Ukur kinerja model pada data test

Melatih model

Impor modul decision tree

from sklearn.tree import DecisionTreeClassifier

Inisialisasi model Decision Tree

mytree = DecisionTreeClassifier()

Latih model pada data train

treemodel = mytree.fit(train_X, train_Y)

Mengukur akurasi model

from sklearn.metrics import accuracy_score

pred_train_Y = mytree.predict(train_X)
pred_test_Y = mytree.predict(test_X)

train_accuracy = accuracy_score(train_Y, pred_train_Y)
test_accuracy = accuracy_score(test_Y, pred_test_Y)

print('Training accuracy:', round(train_accuracy,4))
print('Test accuracy:', round(test_accuracy, 4))

Training accuracy: 0.9973
Test accuracy: 0.7196

Mengukur precision dan recall

from sklearn.metrics import precision_score, recall_score

train_precision = round(precision_score(train_Y, pred_train_Y), 4)
test_precision = round(precision_score(test_Y, pred_test_Y), 4)

train_recall = round(recall_score(train_Y, pred_train_Y), 4)
test_recall = round(recall_score(test_Y, pred_test_Y), 4)

print('Training precision: {}, Training recall: {}'.format(train_precision, train_recall))
print('Test precision: {}, Test recall: {}'.format(train_recall, test_recall))

Training precision: 0.9993, Training recall: 0.9906
Test precision: 0.9906, Test recall: 0.4878

Tuning parameter kedalaman pohon

depth_list = list(range(2,15))
depth_tuning = np.zeros((len(depth_list), 4))
depth_tuning[:,0] = depth_list

for index in range(len(depth_list)):
    mytree = DecisionTreeClassifier(max_depth=depth_list[index])
    mytree.fit(train_X, train_Y)
    pred_test_Y = mytree.predict(test_X)

    depth_tuning[index,1] = accuracy_score(test_Y, pred_test_Y)
    depth_tuning[index,2] = precision_score(test_Y, pred_test_Y)
    depth_tuning[index,3] = recall_score(test_Y, pred_test_Y)

col_names = ['Max_Depth','Accuracy','Precision','Recall']
print(pd.DataFrame(depth_tuning, columns=col_names))

Memilih kedalaman optimal

Tuning Max Depth

Memilih kedalaman optimal

Tuning Max Depth

Mari membangun decision tree!

Machine Learning untuk Pemasaran dengan Python