用決策樹預測流失

Python 的行銷機器學習

Karolis Urbonas

Head of Analytics & Science, Amazon

決策樹入門

鐵達尼號生還資料集的決策樹規則

建模步驟

將資料【分割】為訓練與測試
【初始化】模型
在訓練資料上【訓練】模型
在測試資料上【預測】
在測試資料上【評估】模型效能

模型擬合

匯入決策樹模組

from sklearn.tree import DecisionTreeClassifier

初始化 Decision Tree 模型

mytree = DecisionTreeClassifier()

在訓練資料上擬合模型

treemodel = mytree.fit(train_X, train_Y)

衡量模型準確率

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

衡量精確率與召回率

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

樹深度參數調校

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))

選擇最佳深度

Max Depth 調校

選擇最佳深度

Max Depth 調校

我們來建一棵決策樹！

Python 的行銷機器學習