11.1 AdaBoost模型简单代码实现

1.AdaBoost分类模型演示

from sklearn.ensemble import AdaBoostClassifier
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [0, 0, 0, 1, 1]

model = AdaBoostClassifier(random_state=123)
model.fit(X, y)

print(model.predict([[5, 5]]))

[0]

2.AdaBoost回归模型演示

from sklearn.ensemble import AdaBoostRegressor
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [1, 2, 3, 4, 5]

model = AdaBoostRegressor(random_state=123)
model.fit(X, y)

print(model.predict([[5, 5]]))

[3.]

11.2 案例实战 - AdaBoost信用卡精准营销模型

11.2.2 模型搭建

1.读取数据

import pandas as pd
df = pd.read_excel('信用卡精准营销模型.xlsx')
df.head()

2.提取特征变量和目标变量

X = df.drop(columns='响应') 
y = df['响应']

3.划分训练集和测试集

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=123)

4.模型训练及搭建

from sklearn.ensemble import AdaBoostClassifier
clf = AdaBoostClassifier(random_state=123)
clf.fit(X_train, y_train)

AdaBoostClassifier(random_state=123)

AdaBoostClassifier(random_state=123)

11.2.3 模型预测及评估

# 模型搭建完毕后，通过如下代码预测测试集数据：
y_pred = clf.predict(X_test)
print(y_pred)

[1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0
 1 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1
 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1
 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1
 0 1 0 1 0 0 0 0 0 1 1 0 1 0 1 1 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1
 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1]

# 通过和之前章节类似的代码，我们可以将预测值和实际值进行对比：
a = pd.DataFrame()  # 创建一个空DataFrame 
a['预测值'] = list(y_pred)
a['实际值'] = list(y_test)
a.head()

# 查看预测准确度
from sklearn.metrics import accuracy_score
score = accuracy_score(y_pred, y_test)
print(score)

0.85

#查看预测分类概率
y_pred_proba = clf.predict_proba(X_test)
y_pred_proba[0:5]  # 查看前5项，第一列为分类为0的概率，第二列为分类为1的概率

array([[0.19294615, 0.80705385],
       [0.41359387, 0.58640613],
       [0.42597039, 0.57402961],
       [0.66817389, 0.33182611],
       [0.32850159, 0.67149841]])

%matplotlib inline

# 绘制ROC曲线
from sklearn.metrics import roc_curve
fpr, tpr, thres = roc_curve(y_test.values, y_pred_proba[:,1])
import matplotlib.pyplot as plt
plt.plot(fpr, tpr)
plt.show()

​

​

# 查看AUC值
from sklearn.metrics import roc_auc_score
score = roc_auc_score(y_test, y_pred_proba[:,1])
print(score)

0.9559047909673483

# 查看特征重要性
clf.feature_importances_

array([0.18, 0.2 , 0.36, 0.02, 0.24])

# 通过DataFrame的方式展示特征重要性
features = X.columns  # 获取特征名称
importances = clf.feature_importances_  # 获取特征重要性

# 通过二维表格形式显示
importances_df = pd.DataFrame()
importances_df['特征名称'] = features
importances_df['特征重要性'] = importances
importances_df.sort_values('特征重要性', ascending=False)

11.2.4 模型参数（选学）

# # 分类模型，通过如下代码可以查看官方介绍
# from sklearn.ensemble import AdaBoostClassifier
# AdaBoostClassifier?

# # 回归模型，通过如下代码可以查看官方介绍
# from sklearn.ensemble import AdaBoostRegressor
# AdaBoostRegressor?

11.3 GBDT算法的简单代码实现

1.GBDT分类模型演示

from sklearn.ensemble import GradientBoostingClassifier
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [0, 0, 0, 1, 1]

model = GradientBoostingClassifier(random_state=123)
model.fit(X, y)

print(model.predict([[5, 5]]))

[0]

2.GBDT回归模型演示

from sklearn.ensemble import GradientBoostingRegressor
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [1, 2, 3, 4, 5]

model = GradientBoostingRegressor(random_state=123)
model.fit(X, y)

print(model.predict([[5, 5]]))

[2.54908866]

11.4 GBDT案例实战 - 产品定价模型

11.4.2 模型搭建

1.读取数据

import pandas as pd
df = pd.read_excel('产品定价模型.xlsx')
df.head()

查看各个分类的数据量

df['类别'].value_counts()

技术类    336
教辅类    333
办公类    331
Name: 类别, dtype: int64

df['彩印'].value_counts()

0    648
1    352
Name: 彩印, dtype: int64

df['纸张'].value_counts()

双胶纸    615
铜版纸    196
书写纸    189
Name: 纸张, dtype: int64

2.分类型文本变量处理

将文本内容转为数值，关于LabelEncoder()函数也在将在11.1.2节进行进一步讲解

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['类别'] = le.fit_transform(df['类别'])  # 处理类别

# 将类别一列处理后，我们可以使用value_counts()方法查看转化效果：
df['类别'].value_counts()

1    336
2    333
0    331
Name: 类别, dtype: int64

# 另外一种文本内容转为数值的方法，注意不要再运行完上面的代码后运行，因为上面的内容已经被替代完毕了，如果想尝试，需要重新运行，并且，先运行下面的代码
# df['类别'] = df['类别'].replace({'办公类': 0, '技术类': 1, '教辅类': 2})  
# df['类别'].value_counts()

# 下面我们使用同样的方法处理“纸张”一列：
le = LabelEncoder()
df['纸张'] = le.fit_transform(df['纸张'])

# 此时的表格如下：
df.head()

3.提取特征变量和目标变量

X = df.drop(columns='价格') 
y = df['价格']  

4.划分训练集和测试集

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=123)

5.模型训练及搭建

from sklearn.ensemble import GradientBoostingRegressor
model = GradientBoostingRegressor(random_state=123)
model.fit(X_train, y_train)

GradientBoostingRegressor(random_state=123)

GradientBoostingRegressor(random_state=123)

11.4.3 模型预测及评估

# 模型搭建完毕后，通过如下代码预测测试集数据：
y_pred = model.predict(X_test)
print(y_pred[0:50])

[ 71.15004038  79.56199921  68.21751792  90.78788507  78.88479128
  42.28022702  39.27334177  60.74670841  53.59744659  77.65931771
  80.22295545  76.04437155  79.56199921  58.40372895  79.65245266
  44.27997693  53.18177447  35.31452467  92.1798291   58.40372895
  41.96644278  99.50466356  80.22295545  79.69648341  91.45061741
  42.93885741  42.86973046  75.71824996  48.55203652  62.94185778
  39.47077874  61.54190648  95.18389309  51.88118394  65.1293139
  50.17577837  39.54495179  83.63542315  56.24632221 102.1176112
  48.89080247  49.23639342  33.03502962  52.74862135  35.47220867
  35.00370671  53.9446399   74.62364353  35.31452467  53.9446399 ]

# 通过和之前章节类似的代码，我们可以将预测值和实际值进行对比：
a = pd.DataFrame()  # 创建一个空DataFrame 
a['预测值'] = list(y_pred)
a['实际值'] = list(y_test)
a.head()

# 查看预测评分 - 方法1：自带的score函数，本质就是R-squared值（也即统计学中常说的R^2)
model.score(X_test, y_test)

0.8741691363311168

# 查看预测评分 - 方法2：r2_score()函数
from sklearn.metrics import r2_score
r2 = r2_score(y_test, model.predict(X_test))
print(r2)

0.8741691363311168

# 查看特征重要性
model.feature_importances_

array([0.49070203, 0.44718694, 0.04161545, 0.02049558])

# 通过DataFrame的方式展示特征重要性
features = X.columns  # 获取特征名称
importances = model.feature_importances_  # 获取特征重要性

# 通过二维表格形式显示
importances_df = pd.DataFrame()
importances_df['特征名称'] = features
importances_df['特征重要性'] = importances
importances_df.sort_values('特征重要性', ascending=False)

11.4.4 模型参数(选学)

# # 分类模型，通过如下代码可以查看官方介绍
# from sklearn.ensemble import AdaBoostClassifier
# AdaBoostClassifier?

# # 回归模型，通过如下代码可以查看官方介绍
# from sklearn.ensemble import AdaBoostRegressor
# AdaBoostRegressor?