数据挖掘实战一：输入预测分类
# 导入第三方包
import pandas as pd
import numpy as np
import seaborn as sns
# 数据读取
income = pd.read_excel(r'./income.xlsx')
income.head()
#了解数据的大体结构。输出前几行
| age | workclass | fnlwgt | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | income | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | State-gov | 77516 | Bachelors | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States | <=50K |
| 1 | 50 | Self-emp-not-inc | 83311 | Bachelors | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States | <=50K |
| 2 | 38 | Private | 215646 | HS-grad | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States | <=50K |
| 3 | 53 | Private | 234721 | 11th | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States | <=50K |
| 4 | 28 | Private | 338409 | Bachelors | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba | <=50K |
# 查看数据集是否存在缺失值
income.apply(lambda x:np.sum(x.isnull()))
##info()用来查看数据是否有缺失值，以及数据类型。
income.info()
### 3万条数据
RangeIndex: 32561 entries, 0 to 32560
Data columns (total 15 columns):
age 32561 non-null int64
workclass 30725 non-null object
fnlwgt 32561 non-null int64
education 32561 non-null object
education-num 32561 non-null int64
marital-status 32561 non-null object
occupation 30718 non-null object
relationship 32561 non-null object
race 32561 non-null object
sex 32561 non-null object
capital-gain 32561 non-null int64
capital-loss 32561 non-null int64
hours-per-week 32561 non-null int64
native-country 31978 non-null object
income 32561 non-null object
dtypes: int64(6), object(9)
memory usage: 3.7+ MB
从上可以看出,存在缺失值，workclass，occupation，native-country ,缺失值都是类别值，因此用众数进行填充
# 缺失值处理
income.fillna(value = {'workclass':income.workclass.mode()[0],
'occupation':income.occupation.mode()[0],
'native-country':income['native-country'].mode()[0]}, inplace = True)
income.head()
| age | workclass | fnlwgt | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | income | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | State-gov | 77516 | Bachelors | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States | <=50K |
| 1 | 50 | Self-emp-not-inc | 83311 | Bachelors | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States | <=50K |
| 2 | 38 | Private | 215646 | HS-grad | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States | <=50K |
| 3 | 53 | Private | 234721 | 11th | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States | <=50K |
| 4 | 28 | Private | 338409 | Bachelors | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba | <=50K |
# 数据的探索性分析
income.describe()
#了解数据的大致分布，但是这种分布不包括字符串类型
| age | fnlwgt | education-num | capital-gain | capital-loss | hours-per-week | |
|---|---|---|---|---|---|---|
| count | 32561.000000 | 3.256100e+04 | 32561.000000 | 32561.000000 | 32561.000000 | 32561.000000 |
| mean | 38.581647 | 1.897784e+05 | 10.080679 | 1077.648844 | 87.303830 | 40.437456 |
| std | 13.640433 | 1.055500e+05 | 2.572720 | 7385.292085 | 402.960219 | 12.347429 |
| min | 17.000000 | 1.228500e+04 | 1.000000 | 0.000000 | 0.000000 | 1.000000 |
| 25% | 28.000000 | 1.178270e+05 | 9.000000 | 0.000000 | 0.000000 | 40.000000 |
| 50% | 37.000000 | 1.783560e+05 | 10.000000 | 0.000000 | 0.000000 | 40.000000 |
| 75% | 48.000000 | 2.370510e+05 | 12.000000 | 0.000000 | 0.000000 | 45.000000 |
| max | 90.000000 | 1.484705e+06 | 16.000000 | 99999.000000 | 4356.000000 | 99.000000 |
income.describe(include =[ 'object'])
| workclass | education | marital-status | occupation | relationship | race | sex | native-country | income | |
|---|---|---|---|---|---|---|---|---|---|
| count | 32561 | 32561 | 32561 | 32561 | 32561 | 32561 | 32561 | 32561 | 32561 |
| unique | 8 | 16 | 7 | 14 | 6 | 5 | 2 | 41 | 2 |
| top | Private | HS-grad | Married-civ-spouse | Prof-specialty | Husband | White | Male | United-States | <=50K |
| freq | 24532 | 10501 | 14976 | 5983 | 13193 | 27816 | 21790 | 29753 | 24720 |
绘制不同收入水平下的年龄核密度图
# 导入绘图模块
import matplotlib.pyplot as plt
# 设置绘图风格
plt.style.use('ggplot')
# 设置多图形的组合
fig, axes = plt.subplots(2, 1)
# 绘制不同收入水平下的年龄核密度图
income.age[income.income == ' <=50K'].plot(kind = 'kde', label = '<=50K', ax = axes[0], legend = True, linestyle = '-')
income.age[income.income == ' >50K'].plot(kind = 'kde', label = '>50K', ax = axes[0], legend = True, linestyle = '--')
# 绘制不同收入水平下的周工作小时数和密度图
income['hours-per-week'][income.income == ' <=50K'].plot(kind = 'kde', label = '<=50K', ax = axes[1], legend = True, linestyle = '-')
income['hours-per-week'][income.income == ' >50K'].plot(kind = 'kde', label = '>50K', ax = axes[1], legend = True, linestyle = '--')
# 显示图形
plt.show()
构造不同收入水平下各种族人数的数据
# 构造不同收入水平下各种族人数的数据
race = pd.DataFrame(income.groupby(by = ['race','income']).aggregate(np.size).loc[:,'age'])
# race = pd.DataFrame(income.groupby(by = ['race','income']))
print(race)
# 重设行索引
race = race.reset_index()
# 变量重命名
race.rename(columns={'age':'counts'}, inplace=True)
# 排序
race.sort_values(by = ['race','counts'], ascending=False, inplace=True)
# 构造不同收入水平下各家庭关系人数的数据
relationship = pd.DataFrame(income.groupby(by = ['relationship','income']).aggregate(np.size).loc[:,'age'])
relationship = relationship.reset_index()
relationship.rename(columns={'age':'counts'}, inplace=True)
relationship.sort_values(by = ['relationship','counts'], ascending=False, inplace=True)
print(race)
# 设置图框比例，并绘图
plt.figure(figsize=(9,5))
sns.barplot(x="race", y="counts", hue = 'income', data=race)
plt.show()
plt.figure(figsize=(9,5))
sns.barplot(x="relationship", y="counts", hue = 'income', data=relationship)
plt.show()
age
race income
Amer-Indian-Eskimo <=50K 275
>50K 36
Asian-Pac-Islander <=50K 763
>50K 276
Black <=50K 2737
>50K 387
Other <=50K 246
>50K 25
White <=50K 20699
>50K 7117
race income counts
8 White <=50K 20699
9 White >50K 7117
6 Other <=50K 246
7 Other >50K 25
4 Black <=50K 2737
5 Black >50K 387
2 Asian-Pac-Islander <=50K 763
3 Asian-Pac-Islander >50K 276
0 Amer-Indian-Eskimo <=50K 275
1 Amer-Indian-Eskimo >50K 36
# 离散变量的重编码,重编码是映射为了数字类型
for feature in income.columns:
if income[feature].dtype == 'object':
income[feature] = pd.Categorical(income[feature]).codes
income.head()
| age | workclass | fnlwgt | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | income | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | 6 | 77516 | 9 | 13 | 4 | 0 | 1 | 4 | 1 | 2174 | 0 | 40 | 38 | 0 |
| 1 | 50 | 5 | 83311 | 9 | 13 | 2 | 3 | 0 | 4 | 1 | 0 | 0 | 13 | 38 | 0 |
| 2 | 38 | 3 | 215646 | 11 | 9 | 0 | 5 | 1 | 4 | 1 | 0 | 0 | 40 | 38 | 0 |
| 3 | 53 | 3 | 234721 | 1 | 7 | 2 | 5 | 0 | 2 | 1 | 0 | 0 | 40 | 38 | 0 |
| 4 | 28 | 3 | 338409 | 9 | 13 | 2 | 9 | 5 | 2 | 0 | 0 | 0 | 40 | 4 | 0 |
# 删除变量
income.drop(['education','fnlwgt'], axis = 1, inplace = True)
income.head()
| age | workclass | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | income | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | 6 | 13 | 4 | 0 | 1 | 4 | 1 | 2174 | 0 | 40 | 38 | 0 |
| 1 | 50 | 5 | 13 | 2 | 3 | 0 | 4 | 1 | 0 | 0 | 13 | 38 | 0 |
| 2 | 38 | 3 | 9 | 0 | 5 | 1 | 4 | 1 | 0 | 0 | 40 | 38 | 0 |
| 3 | 53 | 3 | 7 | 2 | 5 | 0 | 2 | 1 | 0 | 0 | 40 | 38 | 0 |
| 4 | 28 | 3 | 13 | 2 | 9 | 5 | 2 | 0 | 0 | 0 | 40 | 4 | 0 |
# 数据拆分
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(income.loc[:,'age':'native-country'],
income['income'], train_size = 0.75,
random_state = 1234)
print('训练数据集共有%d条观测' %X_train.shape[0])
print('测试数据集共有%d条观测' %X_test.shape[0])
训练数据集共有24420条观测
测试数据集共有8141条观测
# 导入k近邻模型的类
from sklearn.neighbors import KNeighborsClassifier
# 构建k近邻模型
kn = KNeighborsClassifier()
kn.fit(X_train, y_train)
print(kn)
# 预测测试集
kn_pred = kn.predict(X_test)
print(pd.crosstab(kn_pred, y_test))
# 模型得分
print('模型在训练集上的准确率%f' %kn.score(X_train,y_train))
print('模型在测试集上的准确率%f' %kn.score(X_test,y_test))
# # 导入模型评估模块
from sklearn import metrics
# 计算ROC曲线的x轴和y轴数据
fpr, tpr, _ = metrics.roc_curve(y_test, kn.predict_proba(X_test)[:,1])
# 绘制ROC曲线
plt.plot(fpr, tpr, linestyle = 'solid', color = 'red')
# 添加阴影
plt.stackplot(fpr, tpr, color = 'steelblue')
# 绘制参考线
plt.plot([0,1],[0,1], linestyle = 'dashed', color = 'black')
# 往图中添加文本
plt.text(0.6,0.4,'AUC=%.3f' % metrics.auc(fpr,tpr), fontdict = dict(size = 18))
plt.show()
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=5, p=2,
weights='uniform')
income 0 1
row_0
0 5637 723
1 589 1192
模型在训练集上的准确率0.890500
模型在测试集上的准确率0.838840
# 导入GBDT模型的类
from sklearn.ensemble import GradientBoostingClassifier
# 构建GBDT模型
gbdt = GradientBoostingClassifier()
gbdt.fit(X_train, y_train)
print(gbdt)
# 预测测试集
gbdt_pred = gbdt.predict(X_test)
print(pd.crosstab(gbdt_pred, y_test))
# 模型得分
print('模型在训练集上的准确率%f' %gbdt.score(X_train,y_train))
print('模型在测试集上的准确率%f' %gbdt.score(X_test,y_test))
# 绘制ROC曲线
fpr, tpr, _ = metrics.roc_curve(y_test, gbdt.predict_proba(X_test)[:,1])
plt.plot(fpr, tpr, linestyle = 'solid', color = 'red')
plt.stackplot(fpr, tpr, color = 'steelblue')
plt.plot([0,1],[0,1], linestyle = 'dashed', color = 'black')
plt.text(0.6,0.4,'AUC=%.3f' % metrics.auc(fpr,tpr), fontdict = dict(size = 18))
plt.show()
GradientBoostingClassifier(criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='auto',
random_state=None, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
income 0 1
row_0
0 5862 784
1 364 1131
模型在训练集上的准确率0.869451
模型在测试集上的准确率0.858985
# K近邻模型的网格搜索法
# 导入网格搜索法的函数
from sklearn.model_selection import GridSearchCV
# 选择不同的参数
k_options = list(range(1,12))
parameters = {'n_neighbors':k_options}
# 搜索不同的K值
grid_kn = GridSearchCV(estimator = KNeighborsClassifier(), param_grid = parameters, cv=10, scoring='accuracy', verbose=0, n_jobs=2)
grid_kn.fit(X_train, y_train)
print(grid_kn)
# 结果输出
grid_kn.cv_results_, grid_kn.best_params_, grid_kn.best_score_
GridSearchCV(cv=10, error_score='raise-deprecating',
estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,
metric='minkowski',
metric_params=None, n_jobs=None,
n_neighbors=5, p=2,
weights='uniform'),
iid='warn', n_jobs=2,
param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring='accuracy', verbose=0)
({'mean_fit_time': array([0.48654635, 0.46757383, 0.47592268, 0.49453475, 0.47880325,
0.46707897, 0.48813548, 0.49772682, 0.47156236, 0.46706924,
0.4772419 ]),
'std_fit_time': array([0.01297885, 0.0209712 , 0.02072921, 0.0111157 , 0.01790766,
0.02473602, 0.0163508 , 0.00986862, 0.0199231 , 0.02273718,
0.03890239]),
'mean_score_time': array([0.13643334, 0.14554381, 0.14788237, 0.14755256, 0.15460474,
0.15969527, 0.15761855, 0.15811694, 0.16488271, 0.16624339,
0.16210868]),
'std_score_time': array([0.00353061, 0.00297507, 0.00272643, 0.00230722, 0.00245015,
0.0026701 , 0.0032928 , 0.0039421 , 0.00378626, 0.00273812,
0.00302414]),
'param_n_neighbors': masked_array(data=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
mask=[False, False, False, False, False, False, False, False,
False, False, False],
fill_value='?',
dtype=object),
'params': [{'n_neighbors': 1},
{'n_neighbors': 2},
{'n_neighbors': 3},
{'n_neighbors': 4},
{'n_neighbors': 5},
{'n_neighbors': 6},
{'n_neighbors': 7},
{'n_neighbors': 8},
{'n_neighbors': 9},
{'n_neighbors': 10},
{'n_neighbors': 11}],
'split0_test_score': array([0.8014736 , 0.82316824, 0.8215309 , 0.8264429 , 0.82889889,
0.83381089, 0.83831355, 0.83503889, 0.83831355, 0.84036021,
0.84322554]),
'split1_test_score': array([0.81416291, 0.83585755, 0.83544822, 0.84936553, 0.84240688,
0.8489562 , 0.84527221, 0.84486287, 0.84240688, 0.84568154,
0.84117888]),
'split2_test_score': array([0.81866558, 0.83790422, 0.84158821, 0.85550553, 0.84568154,
0.85345886, 0.84854687, 0.85100287, 0.84731887, 0.84854687,
0.84690954]),
'split3_test_score': array([0.81539091, 0.84854687, 0.84936553, 0.84936553, 0.84527221,
0.84977487, 0.84568154, 0.84609087, 0.84240688, 0.84486287,
0.8501842 ]),
'split4_test_score': array([0.80917281, 0.83538084, 0.83701884, 0.84316134, 0.83783784,
0.84111384, 0.83783784, 0.83824734, 0.83619984, 0.83865684,
0.83701884]),
'split5_test_score': array([0.82186732, 0.83783784, 0.83619984, 0.84111384, 0.83210483,
0.84029484, 0.83415233, 0.83947584, 0.84234234, 0.84520885,
0.84316134]),
'split6_test_score': array([0.81810733, 0.8467841 , 0.84104875, 0.85866448, 0.85210979,
0.86112249, 0.85006145, 0.85784515, 0.85210979, 0.85088079,
0.84842278]),
'split7_test_score': array([0.82466202, 0.8455551 , 0.84965178, 0.85538714, 0.85743548,
0.86071282, 0.85907415, 0.85743548, 0.86112249, 0.85948382,
0.85538714]),
'split8_test_score': array([0.8095043 , 0.83408439, 0.8275297 , 0.83490373, 0.83941008,
0.84309709, 0.83900041, 0.83981975, 0.84104875, 0.84350676,
0.84391643]),
'split9_test_score': array([0.81482999, 0.83941008, 0.83039738, 0.83818107, 0.83900041,
0.84473576, 0.84514543, 0.84596477, 0.8443261 , 0.8467841 ,
0.84350676]),
'mean_test_score': array([0.81478296, 0.83845209, 0.83697789, 0.84520885, 0.84201474,
0.8477068 , 0.84430794, 0.8455774 , 0.84475839, 0.8463964 ,
0.84529075]),
'std_test_score': array([0.00641223, 0.00701638, 0.00852103, 0.00976708, 0.00816947,
0.00842365, 0.00694003, 0.00746034, 0.00688403, 0.00552323,
0.00487327]),
'rank_test_score': array([11, 9, 10, 5, 8, 1, 7, 3, 6, 2, 4], dtype=int32)},
{'n_neighbors': 6},
0.8477067977067977)
# 预测测试集
grid_kn_pred = grid_kn.predict(X_test)
print(pd.crosstab(grid_kn_pred, y_test))
# 模型得分
print('模型在训练集上的准确率%f' %grid_kn.score(X_train,y_train))
print('模型在测试集上的准确率%f' %grid_kn.score(X_test,y_test))
# 绘制ROC曲线
fpr, tpr, _ = metrics.roc_curve(y_test, grid_kn.predict_proba(X_test)[:,1])
plt.plot(fpr, tpr, linestyle = 'solid', color = 'red')
plt.stackplot(fpr, tpr, color = 'steelblue')
plt.plot([0,1],[0,1], linestyle = 'dashed', color = 'black')
plt.text(0.6,0.4,'AUC=%.3f' % metrics.auc(fpr,tpr), fontdict = dict(size = 18))
plt.show()
income 0 1
row_0
0 5834 867
1 392 1048
模型在训练集上的准确率0.882473
模型在测试集上的准确率0.845351
##### 一共三万条数据
# GBDT模型的网格搜索法
# 选择不同的参数
learning_rate_options = [0.01,0.05,0.1]
max_depth_options = [3,5,7,9]
n_estimators_options = [100,300,500]
parameters = {'learning_rate':learning_rate_options,'max_depth':max_depth_options,'n_estimators':n_estimators_options}
grid_gbdt = GridSearchCV(estimator = GradientBoostingClassifier(), param_grid = parameters, cv=10, scoring='accuracy', n_jobs=4)
grid_gbdt.fit(X_train, y_train)
# 结果输出
grid_gbdt. , grid_gbdt.best_params_, grid_gbdt.best_score_
({'mean_fit_time': array([ 1.04818089, 3.06958101, 4.94570587, 2.10741355, 6.49262218,
10.31357944, 3.79211471, 12.02091534, 19.82360842, 6.47681627,
21.55113347, 36.75011723, 0.98570092, 2.69694235, 4.36460373,
2.08887751, 5.49897749, 8.82655048, 3.94254134, 10.71680896,
17.65818636, 7.33641875, 20.94076185, 36.44300106, 0.93227916,
2.53501928, 4.2528425 , 1.93049173, 5.26935222, 8.83973567,
3.70204911, 10.58135281, 18.04044161, 7.09446683, 22.26043143,
37.91747351]),
'std_fit_time': array([0.00483648, 0.0206611 , 0.02346627, 0.01078171, 0.04006112,
0.08443816, 0.05559104, 0.16459559, 0.27928162, 0.14452495,
0.66920276, 0.75215195, 0.01029911, 0.02504625, 0.07578231,
0.04792665, 0.12168305, 0.20212313, 0.07955249, 0.19383459,
0.27861969, 0.16787211, 0.50177507, 0.5893442 , 0.0070915 ,
0.01666785, 0.08162466, 0.03402036, 0.10060916, 0.15109724,
0.05160359, 0.18684248, 0.14383105, 0.10197265, 0.23977306,
2.26425837]),
'mean_score_time': array([0.00471151, 0.0112366 , 0.0171237 , 0.00647447, 0.0172616 ,
0.0264991 , 0.00875354, 0.02411616, 0.03694515, 0.01122572,
0.0318507 , 0.05002267, 0.00462885, 0.00959365, 0.01390805,
0.00651336, 0.01392448, 0.02110162, 0.00865908, 0.01957636,
0.03109238, 0.01125824, 0.02738936, 0.04511173, 0.00438557,
0.00871301, 0.01321261, 0.00598361, 0.01336231, 0.02128084,
0.00788417, 0.01946959, 0.03210063, 0.01043718, 0.0282335 ,
0.04428184]),
'std_score_time': array([1.66779829e-04, 1.03325180e-04, 1.07752274e-04, 7.75191493e-05,
1.42365449e-04, 2.01649271e-04, 8.73166179e-05, 1.47984253e-04,
2.08206887e-04, 7.54081784e-05, 1.55344452e-04, 2.96993442e-04,
2.14154570e-05, 4.05286409e-05, 1.29071777e-04, 8.44396348e-05,
5.86331590e-05, 1.71900728e-04, 4.77111023e-05, 1.84118423e-04,
3.62546530e-04, 9.20189008e-05, 2.26528819e-04, 3.68539617e-04,
2.51831874e-05, 6.76646881e-05, 1.10660028e-04, 6.27695447e-05,
1.87586799e-04, 2.69605476e-04, 4.36938978e-05, 2.31657194e-04,
2.70279819e-04, 6.23716901e-05, 2.04953060e-04, 5.62525009e-03]),
'param_learning_rate': masked_array(data=[0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object),
'param_max_depth': masked_array(data=[3, 3, 3, 5, 5, 5, 7, 7, 7, 9, 9, 9, 3, 3, 3, 5, 5, 5,
7, 7, 7, 9, 9, 9, 3, 3, 3, 5, 5, 5, 7, 7, 7, 9, 9, 9],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object),
'param_n_estimators': masked_array(data=[100, 300, 500, 100, 300, 500, 100, 300, 500, 100, 300,
500, 100, 300, 500, 100, 300, 500, 100, 300, 500, 100,
300, 500, 100, 300, 500, 100, 300, 500, 100, 300, 500,
100, 300, 500],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object),
'params': [{'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 100},
{'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 300},
{'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 500},
{'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100},
{'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 300},
{'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 500},
{'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 100},
{'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 300},
{'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 500},
{'learning_rate': 0.01, 'max_depth': 9, 'n_estimators': 100},
{'learning_rate': 0.01, 'max_depth': 9, 'n_estimators': 300},
{'learning_rate': 0.01, 'max_depth': 9, 'n_estimators': 500},
{'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 100},
{'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 300},
{'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 500},
{'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 100},
{'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 300},
{'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 500},
{'learning_rate': 0.05, 'max_depth': 7, 'n_estimators': 100},
{'learning_rate': 0.05, 'max_depth': 7, 'n_estimators': 300},
{'learning_rate': 0.05, 'max_depth': 7, 'n_estimators': 500},
{'learning_rate': 0.05, 'max_depth': 9, 'n_estimators': 100},
{'learning_rate': 0.05, 'max_depth': 9, 'n_estimators': 300},
{'learning_rate': 0.05, 'max_depth': 9, 'n_estimators': 500},
{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100},
{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 300},
{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 500},
{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100},
{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 300},
{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 500},
{'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 100},
{'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 300},
{'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 500},
{'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 100},
{'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 300},
{'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 500}],
'split0_test_score': array([0.82685223, 0.83422022, 0.84404421, 0.83381089, 0.84445354,
0.8514122 , 0.83667622, 0.8465002 , 0.84854687, 0.83381089,
0.84322554, 0.84322554, 0.84322554, 0.8514122 , 0.85427753,
0.8526402 , 0.85345886, 0.85550553, 0.84813754, 0.84977487,
0.84813754, 0.83954155, 0.84527221, 0.83626688, 0.8489562 ,
0.85345886, 0.85059353, 0.85304953, 0.85059353, 0.85223086,
0.84936553, 0.84609087, 0.84281621, 0.84445354, 0.83790422,
0.83462955]),
'split1_test_score': array([0.85304953, 0.86614818, 0.86860418, 0.86410151, 0.8714695 ,
0.8726975 , 0.86410151, 0.87556283, 0.87883749, 0.86000819,
0.8739255 , 0.87597217, 0.86901351, 0.87228817, 0.87679083,
0.87474417, 0.87924683, 0.88129349, 0.87842816, 0.88088416,
0.87842816, 0.87679083, 0.87310684, 0.86573885, 0.86778551,
0.87965616, 0.88252149, 0.87679083, 0.87679083, 0.87842816,
0.88047483, 0.8739255 , 0.8726975 , 0.8751535 , 0.86369218,
0.86000819]),
'split2_test_score': array([0.84486287, 0.85714286, 0.86369218, 0.84977487, 0.86942284,
0.87187884, 0.85509619, 0.8751535 , 0.87597217, 0.85304953,
0.87351617, 0.87842816, 0.86369218, 0.87474417, 0.8763815 ,
0.87187884, 0.87679083, 0.87556283, 0.87433483, 0.8751535 ,
0.8726975 , 0.87597217, 0.87228817, 0.86778551, 0.86983217,
0.8763815 , 0.8763815 , 0.87351617, 0.87679083, 0.87187884,
0.87597217, 0.87351617, 0.86860418, 0.87474417, 0.86410151,
0.85509619]),
'split3_test_score': array([0.83872288, 0.85509619, 0.86287352, 0.84527221, 0.87024151,
0.8776095 , 0.85468686, 0.88006549, 0.88538682, 0.85918952,
0.87597217, 0.87965616, 0.86369218, 0.87228817, 0.8763815 ,
0.87801883, 0.88415882, 0.88620549, 0.88374949, 0.88620549,
0.88252149, 0.88047483, 0.88293082, 0.86983217, 0.87065084,
0.8763815 , 0.88129349, 0.88211216, 0.88538682, 0.88088416,
0.88661482, 0.87842816, 0.87065084, 0.88252149, 0.87024151,
0.86041752]),
'split4_test_score': array([0.85176085, 0.86322686, 0.86936937, 0.86076986, 0.86895987,
0.87755938, 0.85995086, 0.87223587, 0.87592138, 0.85462735,
0.87264537, 0.87018837, 0.86895987, 0.87510238, 0.87510238,
0.87960688, 0.87592138, 0.87387387, 0.87633088, 0.87428337,
0.87141687, 0.87100737, 0.85913186, 0.86036036, 0.87469287,
0.87469287, 0.87264537, 0.87633088, 0.87305487, 0.87387387,
0.87674038, 0.86732187, 0.85954136, 0.86363636, 0.85135135,
0.84602785]),
'split5_test_score': array([0.83701884, 0.84930385, 0.85462735, 0.84930385, 0.85667486,
0.86977887, 0.85012285, 0.86486486, 0.87346437, 0.84889435,
0.87305487, 0.87510238, 0.85421785, 0.86977887, 0.87469287,
0.87100737, 0.87510238, 0.87551188, 0.87469287, 0.87346437,
0.87346437, 0.87469287, 0.87305487, 0.86527437, 0.86445536,
0.87674038, 0.87714988, 0.87346437, 0.87755938, 0.87305487,
0.87837838, 0.87469287, 0.86773137, 0.87387387, 0.86445536,
0.85094185]),
'split6_test_score': array([0.84719377, 0.85702581, 0.8648095 , 0.85661614, 0.86603851,
0.8738222 , 0.85948382, 0.87832855, 0.88529291, 0.85948382,
0.87709955, 0.8816059 , 0.8648095 , 0.8738222 , 0.88365424,
0.87546088, 0.88652192, 0.88406391, 0.88324457, 0.88652192,
0.88365424, 0.8816059 , 0.87996723, 0.8725932 , 0.86972552,
0.88529291, 0.88816059, 0.88406391, 0.88365424, 0.8828349 ,
0.88611225, 0.88447358, 0.86972552, 0.8828349 , 0.86603851,
0.86317083]),
'split7_test_score': array([0.8455551 , 0.85415813, 0.85702581, 0.85620647, 0.86153216,
0.86644818, 0.85702581, 0.87054486, 0.8725932 , 0.85661614,
0.87218353, 0.87423187, 0.85702581, 0.86685785, 0.86808685,
0.86685785, 0.87013519, 0.87177386, 0.87587054, 0.87668988,
0.8725932 , 0.87750922, 0.86849652, 0.86439984, 0.8635805 ,
0.86849652, 0.87054486, 0.87218353, 0.87505121, 0.87668988,
0.87505121, 0.86931585, 0.86644818, 0.87177386, 0.85948382,
0.85866448]),
'split8_test_score': array([0.84104875, 0.85006145, 0.8533388 , 0.84842278, 0.8545678 ,
0.85989349, 0.84883245, 0.86030315, 0.86562884, 0.84965178,
0.85866448, 0.8623515 , 0.85374846, 0.86030315, 0.86808685,
0.85907415, 0.86972552, 0.86849652, 0.86685785, 0.86399017,
0.8635805 , 0.8648095 , 0.86030315, 0.85497747, 0.85825481,
0.86644818, 0.86726751, 0.86521917, 0.86644818, 0.86767718,
0.86439984, 0.8623515 , 0.85866448, 0.86439984, 0.85497747,
0.83981975]),
'split9_test_score': array([0.84063908, 0.85292913, 0.85661614, 0.84924211, 0.86071282,
0.86849652, 0.85210979, 0.87095453, 0.8725932 , 0.85292913,
0.87095453, 0.87587054, 0.85866448, 0.87054486, 0.87709955,
0.86931585, 0.87832855, 0.87996723, 0.87218353, 0.87587054,
0.87218353, 0.87300287, 0.87300287, 0.87013519, 0.86521917,
0.87546088, 0.87791889, 0.87587054, 0.87750922, 0.87505121,
0.87587054, 0.8738222 , 0.86767718, 0.86931585, 0.86030315,
0.86153216]),
'mean_test_score': array([0.84266994, 0.8539312 , 0.85950041, 0.85135135, 0.86240786,
0.86895987, 0.85380835, 0.86945127, 0.87342342, 0.85282555,
0.86912367, 0.87166257, 0.85970516, 0.86871417, 0.87305487,
0.86986077, 0.87493857, 0.87522523, 0.87338247, 0.87428337,
0.87186732, 0.87153972, 0.86875512, 0.86273546, 0.86531532,
0.87330057, 0.87444717, 0.87325962, 0.87428337, 0.87325962,
0.87489762, 0.87039312, 0.86445536, 0.87027027, 0.85925471,
0.8530303 ]),
'std_test_score': array([0.00727039, 0.00826494, 0.0074335 , 0.00816746, 0.00818114,
0.00769633, 0.00723565, 0.00950916, 0.01003632, 0.00735759,
0.00986804, 0.01077537, 0.00758753, 0.00712003, 0.00757436,
0.00802774, 0.00874896, 0.00840377, 0.00963988, 0.01025699,
0.00965164, 0.01160156, 0.01053757, 0.0100632 , 0.0069548 ,
0.00827922, 0.00981026, 0.00835648, 0.00932593, 0.00816926,
0.01036404, 0.00990618, 0.00838173, 0.01055096, 0.00880306,
0.0093857 ]),
'rank_test_score': array([36, 31, 29, 35, 27, 21, 32, 19, 7, 34, 20, 14, 28, 23, 12, 18, 2,
1, 8, 5, 13, 15, 22, 26, 24, 9, 4, 10, 5, 10, 3, 16, 25, 17,
30, 33], dtype=int32)},
{'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 500},
0.8752252252252253)
# 预测测试集
grid_gbdt_pred = grid_gbdt.predict(X_test)
print(pd.crosstab(grid_gbdt_pred, y_test))
# 模型得分
print('模型在训练集上的准确率%f' %grid_gbdt.score(X_train,y_train))
print('模型在测试集上的准确率%f' %grid_gbdt.score(X_test,y_test))
# 绘制ROC曲线
fpr, tpr, _ = metrics.roc_curve(y_test, grid_gbdt_pred)
plt.plot(fpr, tpr, linestyle = 'solid', color = 'red')
plt.stackplot(fpr, tpr, color = 'steelblue')
plt.plot([0,1],[0,1], linestyle = 'dashed', color = 'black')
plt.text(0.6,0.4,'AUC=%.3f' % metrics.auc(fpr,tpr), fontdict = dict(size = 18))
plt.show()
income 0 1
row_0
0 5833 655
1 393 1260
模型在训练集上的准确率0.897379
模型在测试集上的准确率0.871269
代码来自：
从零开始学python数据挖掘与分析第二章