用决策树预测流失

Python 营销中的机器学习

Karolis Urbonas

Head of Analytics & Science, Amazon

决策树简介

泰坦尼克生存数据集的决策树规则

建模步骤

将数据划分为训练集和测试集
初始化模型
在训练集上拟合模型
在测试集上预测
在测试集上评估性能

拟合模型

导入决策树模块

from sklearn.tree import DecisionTreeClassifier

初始化决策树模型

mytree = DecisionTreeClassifier()

在训练集上拟合模型

treemodel = mytree.fit(train_X, train_Y)

评估模型准确率

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))

训练集准确率: 0.9973
测试集准确率: 0.7196

评估精确率与召回率

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))

训练集精确率: 0.9993，训练集召回率: 0.9906
测试集精确率: 0.9906，测试集召回率: 0.4878

树深度参数调优

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))

选择最优深度

最大深度调优

选择最优深度

最大深度调优

让我们构建一个决策树！

Python 营销中的机器学习