集成学习01_xgboost参数讲解与实战

本章分以下几块来讲解

一.xgboost 模型参数介绍

二.xgboost 两种方式实现

三. 网格搜索最优xgboost参数

一.XGBoost的参数

XGBoost的作者把所有的参数分成了三类，这里只介绍我们常用的一些参数，不常用的不做介绍

通用参数：宏观函数控制。
Booster参数：控制每一步的booster(tree/regression)。
学习目标参数：控制训练目标的表现。

1 通用参数

1）booster[默认gbtree]

选择每次迭代的模型，有两种选择：
gbtree：基于树的模型
gbliner：线性模型

2）silent[默认0]

当这个参数值为1时，静默模式开启，不会输出任何信息。
一般这个参数就保持默认的0，因为这样能帮我们更好地理解模型。

3）nthread[默认值为最大可能的线程数]

这个参数用来进行多线程控制，应当输入系统的核数。
如果你希望使用CPU全部的核，那就不要输入这个参数，算法会自动检测它。

4）num_feature [set automatically by xgboost, no need to be set by user]

boosting过程中用到的特征维数，设置为特征个数。
XGBoost会自动设置，不需要手工设置。

2 booster参数

尽管有两种booster可供选择，我这里只介绍tree booster，因为它的表现远远胜过linear booster，所以linear booster很少用到。

1）eta[默认0.3]

和GBM中的 learning rate 参数类似。
通过减少每一步的权重，可以提高模型的鲁棒性。
典型值为0.01-0.2。

2）min_child_weight[默认1]

*决定最小叶子节点样本权重和。

和GBM的 min_child_leaf 参数类似，但不完全一样。XGBoost的这个参数是最小样本权重的和，而GBM参数是最小样本总数。
这个参数用于避免过拟合。当它的值较大时，可以避免模型学习到局部的特殊样本。
但是如果这个值过高，会导致欠拟合。这个参数需要使用CV来调整。

3）max_depth[默认6]

和GBM中的参数相同，这个值为树的最大深度。
这个值也是用来避免过拟合的。max_depth越大，模型会学到更具体更局部的样本。
需要使用CV函数来进行调优。
典型值：3-10

4）max_leaf_nodes

树上最大的节点或叶子的数量。
可以替代max_depth的作用。因为如果生成的是二叉树，一个深度为n的树最多生成
如果定义了这个参数，GBM会忽略max_depth参数。

5）gamma[默认0]

在节点分裂时，只有分裂后损失函数的值下降了，才会分裂这个节点。
Gamma指定了节点分裂所需的最小损失函数下降值。这个参数的值越大，算法越保守。这个参数的值和损失函数息息相关，所以是需要调整的。
模型在默认情况下，对于一个节点的划分只有在其loss function 得到结果大于0的情况下才进行，而gamma 给定了所需的最低loss function的值
gamma值使得算法更conservation，且其值依赖于loss function ，在模型中应该进行调参.

6）max_delta_step[默认0]

这参数限制每棵树权重改变的最大步长。如果这个参数的值为0，那就意味着没有约束。如果它被赋予了某个正值，那么它会让这个算法更加保守。
通常，这个参数不需要设置。但是当各类别的样本十分不平衡时，它对逻辑回归是很有帮助的。
这个参数一般用不到，但是你可以挖掘出来它更多的用处。

7）subsample[默认1]

和GBM中的subsample参数一模一样。这个参数控制对于每棵树，随机采样的比例。
减小这个参数的值，算法会更加保守，避免过拟合。但是，如果这个值设置得过小，它可能会导致欠拟合。
典型值：0.5-1

8）colsample_bytree[默认1]

和GBM里面的max_features参数类似。用来控制每棵随机采样的列数的占比(每一列是一个特征)。
典型值：0.5-1

9）colsample_bylevel[默认1]

用来控制树的每一级的每一次分裂，对列数的采样的占比。
一般不太用这个参数，因为subsample参数和colsample_bytree参数可以起到相同的作用。但是如果感兴趣，可以挖掘这个参数更多的用处。

10）lambda[默认1]

权重的L2正则化项。(和Ridge regression类似)。
这个参数是用来控制XGBoost的正则化部分的。

11）alpha[默认1]

权重的L1正则化项。(和Lasso regression类似)。
可以应用在很高维度的情况下，使得算法的速度更快。

12）scale_pos_weight[默认1]

在各类别样本十分不平衡时，把这个参数设定为一个正值，可以使算法更快收敛。
大于0的取值可以处理类别不平衡的情况。帮助模型更快收敛。

13) Parameter for Linear Booster

lambda_bias

在偏置上的L2正则。缺省值为0（在L1上没有偏置项的正则，因为L1时偏置不重要）

3 学习目标参数

这个参数用来控制理想的优化目标和每一步结果的度量方法。

1）objective[默认reg:linear]

这个参数定义需要被最小化的损失函数。最常用的值有：定义学习任务及相应的学习目标，可选的目标函数如下：
“reg:linear” –线性回归。
“reg:logistic” –逻辑回归。
“binary:logistic” –二分类的逻辑回归问题，输出为概率。
“binary:logitraw” –二分类的逻辑回归问题，输出的结果为wTx。
“count:poisson” –计数问题的poisson回归，输出结果为poisson分布。
在poisson回归中，max_delta_step的缺省值为0.7。(used to safeguard optimization)
“multi:softmax” –让XGBoost采用softmax目标函数处理多分类问题，同时需要设置参数num_class（类别个数）
“multi:softprob” –和softmax一样，但是输出的是ndata * nclass的向量，可以将该向量reshape成ndata行nclass列的矩阵。每行数据表示样本所属于每个类别的概率。
“rank:pairwise” –set XGBoost to do ranking task by minimizing the pairwise loss

2）eval_metric[默认值取决于objective参数的取值]

对于有效数据的度量方法。
对于回归问题，默认值是rmse，对于分类问题，默认值是error。
典型值有：
rmse 均方根误差
mae 平均绝对误差
logloss 负对数似然函数值
error 二分类错误率(阈值为0.5)
merror 多分类错误率
mlogloss 多分类logloss损失函数
auc 曲线下面积

3）seed(默认0)

随机数的种子
设置它可以复现随机数据的结果，也可以用于调整参数
如果你比较习惯scikit-learn的参数形式，那么XGBoost的Python 版本也提供了sklearn形式的接口 XGBClassifier。

4）sklearn 参数对照

它使用sklearn形式的参数命名方式，对应关系如下：

1、eta -> learning_rate
2、lambda -> reg_lambda
3、alpha -> reg_alpha

4.平台控制参数 Console Parameters

The following parameters are only used in the console version of xgboost

use_buffer [ default=1 ]
是否为输入创建二进制的缓存文件，缓存文件可以加速计算。缺省值为1
num_round
boosting迭代计算次数。
data
输入数据的路径
test:data
测试数据的路径
save_period [default=0]
表示保存第i*save_period次迭代的模型。例如save_period=10表示每隔10迭代计算XGBoost将会保存中间结果，设置为0表示每次计算的模型都要保持。
task [default=train] options: train, pred, eval, dump
train：训练模型
pred：对测试数据进行预测
eval：通过eval[name]=filenam定义评价指标
dump：将学习模型保存成文本格式
model_in [default=NULL]
指向模型的路径在test, eval, dump都会用到，如果在training中定义XGBoost将会接着输入模型继续训练
model_out [default=NULL]
训练完成后模型的保存路径，如果没有定义则会输出类似0003.model这样的结果，0003是第三次训练的模型结果。
model_dir [default=models]
输出模型所保存的路径。
fmap
feature map, used for dump model
name_dump [default=dump.txt]
name of model dump file
name_pred [default=pred.txt]
预测结果文件
pred_margin [default=0]
输出预测的边界，而不是转换后的概率

二.xgboost 实现

本章以优惠券推荐数据为例对xgboost结合skleran与直接采用xgboost进行实现

1.导入相关包


import pandas as pd, numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn import metrics
import catboost as cb
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
import os
import joblib
from sklearn.preprocessing import LabelEncoder
from collections import defaultdict
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data=pd.read_excel('car_coupon.xlsx')
data.head(5)
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	ID	destination	passanger	toCoupon_GEQ15min	direction_same	direction_opp	gender	age	maritalStatus	...	Bar	CoffeeHouse	CarryAway	RestaurantLessThan20	Restaurant20To50	weather	time	coupon	expiration	Y
0	11263	No Urgent Place	Friend(s)	0	0	1	Male	55	Widowed	...	0	0	1	1	1	Sunny	14	Coffee House	24	1
1	20136	Work	Alone	1	1	0	Female	26	Married partner	...	0	0	3	3	3	Sunny	7	Bar	24	0
2	14763	Work	Alone	1	0	1	Female	55	Single	...	0	0	1	1	1	Sunny	7	Coffee House	24	0
3	12612	No Urgent Place	Kid(s)	1	0	1	Female	41	Married partner	...	0	3	3	3	3	Sunny	10	Carry out & Take away	2	0
4	17850	No Urgent Place	Partner	1	0	1	Female	31	Married partner	...	1	1	10	10	10	Snowy	14	Coffee House	2	0

5 rows × 23 columns

2.数据处理

1. 对类别数据进行编码

d = defaultdict(LabelEncoder)
data[['destination', 'passanger','gender','maritalStatus','education', 'occupation', \
    'weather','coupon' ]]=data[['destination', 'passanger','gender','maritalStatus','education', 'occupation', \
        'weather','coupon' ]].apply(lambda x: d[x.name].fit_transform(x))
data.head(5)
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	ID	destination	passanger	toCoupon_GEQ15min	direction_same	direction_opp	gender	age	maritalStatus	...	Bar	CoffeeHouse	CarryAway	RestaurantLessThan20	Restaurant20To50	weather	time	coupon	expiration	Y
0	11263	1	1	0	0	1	1	55	4	...	0	0	1	1	1	2	14	2	24	1
1	20136	2	0	1	1	0	0	26	1	...	0	0	3	3	3	2	7	0	24	0
2	14763	2	0	1	0	1	0	55	2	...	0	0	1	1	1	2	7	2	24	0
3	12612	1	2	1	0	1	0	41	1	...	0	3	3	3	3	2	10	1	2	0
4	17850	1	3	1	0	1	0	31	1	...	1	1	10	10	10	1	14	2	2	0

5 rows × 23 columns

切分训练集与测试集

train, test, y_train, y_test = train_test_split(data.drop(["Y"], axis=1), data["Y"],
                                                random_state=10, test_size=0.3)  
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注意下 data ，train, test, y_train, y_test的数据格式

print(type(data))
print(type(train))
print(type( test))
print(type(y_train))
print(type(y_test))
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撰写评价函数

def model_eval2(m, train, test):
    print('train_roc_auc_score:',metrics.roc_auc_score(y_train, m.predict_proba(train)[:, 1]))
    print('test_roc_auc_score:',metrics.roc_auc_score(y_test, m.predict_proba(test)[:, 1]))
    print('train_accuracy_score:',metrics.accuracy_score(y_train,  m.predict(train)))
    print('test_accuracy_score:',metrics.accuracy_score(y_test, m.predict(test)))
    print('train_precision_score:',metrics.precision_score(y_train, m.predict(train)))
    print('test__precision_score:',metrics.precision_score(y_test, m.predict(test)))
    print('train_recall_score:',metrics.recall_score(y_train, m.predict(train)))
    print('test_recall_score:',metrics.recall_score(y_test, m.predict(test)))
    print('train_f1_score:',metrics.f1_score(y_train, m.predict(train)))
    print('test_f1_score:',metrics.f1_score(y_test, m.predict(test)))  
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3.结合sklearn的xgboot模型

step01-拟合模型

from xgboost.sklearn import XGBClassifier
xgboost_model = XGBClassifier()
eval_set = [(test.values,  y_test.values)]
#拟合模型
xgboost_model.fit(train.values, 
                  y_train.values, 
                  early_stopping_rounds=300, 
                  eval_metric="logloss",  # 损失函数的类型，分类一般都是用对数作为损失函数
                  eval_set=eval_set,
                  verbose=False)
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D:\dprograme\Anaconda3\lib\site-packages\xgboost\sklearn.py:793: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.
  warnings.warn(
D:\dprograme\Anaconda3\lib\site-packages\xgboost\sklearn.py:793: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.
  warnings.warn(
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XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
          colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
          early_stopping_rounds=None, enable_categorical=False,
          eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
          importance_type=None, interaction_constraints='',
          learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
          max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
          missing=nan, monotone_constraints='()', n_estimators=100,
          n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
          reg_alpha=0, reg_lambda=1, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

step02-评价模型

model_eval2(xgboost_model, train.values, test.values)
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train_roc_auc_score: 0.890295988831706
test_roc_auc_score: 0.7178983466569767
train_accuracy_score: 0.8007142857142857
test_accuracy_score: 0.6683333333333333
train_precision_score: 0.7965116279069767
test__precision_score: 0.704225352112676
train_recall_score: 0.8681875792141952
test_recall_score: 0.7267441860465116
train_f1_score: 0.8308065494238933
test_f1_score: 0.7153075822603719
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step03-利用模型预测

xgboost_model.predict 预测结果是0或1的int型
xgboost_model.predict_proba预测结果是0到1之间的float型

y_test_pred = xgboost_model.predict( test.values )
y_trian_prod = xgboost_model.predict_proba( train.values )
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step04-保存和调用模型

joblib.dump(xgboost_model , r'D:\Ensemble_Learning\xgboostinfo\xgboostsklearnsingle.model')
load_model=joblib.load(r'D:\Ensemble_Learning\xgboostinfo\xgboostsklearnsingle.model')
load_model.predict( test.values )
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array([0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,
       1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,
       1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1,
       0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,
       1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,
       0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0,
       1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,
       1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
       1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,
       1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
       0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0,
       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,
       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,
       1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,
       0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
       1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
       1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,
       1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0,
       1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,
       0, 1, 1, 0, 1, 0])
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注意点

上面xgboost_model.fit传入的是train.values和y_train.values,数据类型为numpy.ndarray
上面* xgboost_model.predict与xgboost_model.predict_proba传入的数据类型为numpy.ndarray

print(type(data.values))
print(type(train.values))
print(type( test.values))
print(type(y_train.values))
print(type(y_test.values))
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4.直接采用xgboost的模型

step-01 构建参数

params={'alpha': 0.09,
 'booster': 'gbtree',
 'colsample_bylevel': 0.4,
 'colsample_bytree': 0.7,
 'eval_metric': 'logloss',
 'gamma': 0.85,
 'learning_rate': 0.1,
 'max_depth': 7,
 'min_child_weight': 20,
 'n_estimator': 40,
 'objective': 'binary:logistic',
 'reg_lambda': 0.1,
 'seed': 1,
 'subsample': 0.6}
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step-02 处理数据

dtrain = xgb.DMatrix(train, label=y_train,feature_names=list(train.columns))
dtest = xgb.DMatrix(test)
validation = xgb.DMatrix(test,y_test)
watchlist = [(validation,'train')]
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step-03 拟合模型

model = xgb.train(params,
                  dtrain,
                  num_boost_round= 2000, # 迭代的次数，及弱学习器的个数
                  evals= watchlist)
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[21:06:30] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: 
Parameters: { "n_estimator" } might not be used.

  This could be a false alarm, with some parameters getting used by language bindings but
  then being mistakenly passed down to XGBoost core, or some parameter actually being used
  but getting flagged wrongly here. Please open an issue if you find any such cases.


[0]	train-logloss:0.68835
[1]	train-logloss:0.68565
[2]	train-logloss:0.68298
[3]	train-logloss:0.67752
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step-04 用模型预测

ytrain=model.predict(dtrain)
1

注意：

这里model.predict()预测得到的是概率值，而不是0或者1的结果
下面将结果转换为0或者1

ytrain_class = (ytrain>= 0.5)*1
1

ytest=model.predict(dtest)
y_pred = (ytest >= 0.5)*1
1
2

step-05 评价模型效果

print(‘train_roc_auc_score:’,metrics.roc_auc_score(y_train,ytrain))
print(‘test_roc_auc_score:’,metrics.roc_auc_score(y_test, ytest))
print(‘train_accuracy_score:’,metrics.accuracy_score(y_train, ytrain_class))
print(‘test_accuracy_score:’,metrics.accuracy_score(y_test,y_pred ))

step-06 保存模型并调用

joblib.dump(model , r'D:\Ensemble_Learning\xgboostinfo\xgboost01.model')
load_model=joblib.load(r'D:\Ensemble_Learning\xgboostinfo\xgboost01.model')
ytest=load_model.predict(dtest)
ytest[0:5]
1
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4

array([0.265046  , 0.39359182, 0.82298654, 0.07664716, 0.28468448],
      dtype=float32)
1
2

三. 网格搜索最优xgboost参数

1.step-01 配置参数列表

from sklearn.model_selection import GridSearchCV
## 定义参数取值范围
learning_rate = [0.1] #0.15,0.11
subsample = [ 0.65] #0.7,0.8
colsample_bytree = [0.6] #0.7, 0.5
colsample_bylevel=[0.7] #0.8,
colsample_bynode=[0.7] #0.8,
max_depth = [6] #,7
n_estimators=[1000] #,900
gamma=[0,0.1]
reg_alpha=[1,2]
reg_lambda=[2,3]
min_child_weight=[30,50]
max_bin=[12,16]
base_score=[0.4,0.5,0.6]

parameters = { 
              'learning_rate': learning_rate,
              'subsample': subsample,
              'colsample_bytree':colsample_bytree,
              'colsample_bylevel':colsample_bylevel,
              'colsample_bynode':colsample_bynode,
              'max_depth': max_depth,
              'n_estimators':n_estimators,
               'gamma':gamma,
               'reg_alpha':reg_alpha,
               'reg_lambda':reg_lambda,
               'min_child_weight':min_child_weight,
               'max_bin':max_bin,
               'base_score':base_score,
              }

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step-02 选择待优化模型

model = XGBClassifier( eval_metric="logloss")
1

step-03 进行网格搜索拟合模型

clf = GridSearchCV(model, parameters, cv=2, scoring='accuracy',verbose=1,n_jobs=-1)
clf = clf.fit(train.values, y_train.values,eval_set=eval_set)
1
2

Fitting 2 folds for each of 96 candidates, totalling 192 fits
[0]	validation_0-logloss:0.68822
[1]	validation_0-logloss:0.68488
[2]	validation_0-logloss:0.67979
[3]	validation_0-logloss:0.67770
[4]	validation_0-logloss:0.67431
[5]	validation_0-logloss:0.67095
[6]	validation_0-logloss:0.66894
[7]	validation_0-logloss:0.66736
[8]	validation_0-logloss:0.66269
[9]	validation_0-logloss:0.65911
[10]	validation_0-logloss:0.65691
[11]	validation_0-logloss:0.65429
[12]	validation_0-logloss:0.64994
[13]	validation_0-logloss:0.64843
[14]	validation_0-logloss:0.64748
[15]	validation_0-logloss:0.64628
[16]	validation_0-logloss:0.64424
[17]	validation_0-logloss:0.64260
[18]	validation_0-logloss:0.64172
[19]	validation_0-logloss:0.64020
[20]	validation_0-logloss:0.63933
[21]	validation_0-logloss:0.63795
[22]	validation_0-logloss:0.63296
[23]	validation_0-logloss:0.63192
[24]	validation_0-logloss:0.63157
[25]	validation_0-logloss:0.63006
[26]	validation_0-logloss:0.62925
[27]	validation_0-logloss:0.62915
[28]	validation_0-logloss:0.62914
[29]	validation_0-logloss:0.62940
[30]	validation_0-logloss:0.62872
[31]	validation_0-logloss:0.62866
[32]	validation_0-logloss:0.62860
[33]	validation_0-logloss:0.62812
[34]	validation_0-logloss:0.62823
[35]	validation_0-logloss:0.62819
[36]	validation_0-logloss:0.62489
[37]	validation_0-logloss:0.62490
[38]	validation_0-logloss:0.62293
[39]	validation_0-logloss:0.62222
[40]	validation_0-logloss:0.62102
[41]	validation_0-logloss:0.61937
[42]	validation_0-logloss:0.61839
[43]	validation_0-logloss:0.61829
[44]	validation_0-logloss:0.61782
[45]	validation_0-logloss:0.61781
[46]	validation_0-logloss:0.61763
[47]	validation_0-logloss:0.61733
[48]	validation_0-logloss:0.61704
[49]	validation_0-logloss:0.61602
[50]	validation_0-logloss:0.61585
[51]	validation_0-logloss:0.61632
[52]	validation_0-logloss:0.61601
[53]	validation_0-logloss:0.61658
[54]	validation_0-logloss:0.61598
[55]	validation_0-logloss:0.61581
[56]	validation_0-logloss:0.61530
[57]	validation_0-logloss:0.61455
[58]	validation_0-logloss:0.61557
[59]	validation_0-logloss:0.61533
[60]	validation_0-logloss:0.61390
[61]	validation_0-logloss:0.61426
[62]	validation_0-logloss:0.61365
[63]	validation_0-logloss:0.61269
[64]	validation_0-logloss:0.61244
[65]	validation_0-logloss:0.61196
[66]	validation_0-logloss:0.61196
[67]	validation_0-logloss:0.61175
[68]	validation_0-logloss:0.61179
[69]	validation_0-logloss:0.61195
[70]	validation_0-logloss:0.61165
[71]	validation_0-logloss:0.61130
[72]	validation_0-logloss:0.61112
[73]	validation_0-logloss:0.61133
[74]	validation_0-logloss:0.61152
[75]	validation_0-logloss:0.61118
[76]	validation_0-logloss:0.61160
[77]	validation_0-logloss:0.61167
[78]	validation_0-logloss:0.61175
[79]	validation_0-logloss:0.61156
[80]	validation_0-logloss:0.61164
[81]	validation_0-logloss:0.61126
[82]	validation_0-logloss:0.61166
[83]	validation_0-logloss:0.61163
[84]	validation_0-logloss:0.61156
[85]	validation_0-logloss:0.61177
[86]	validation_0-logloss:0.61271
[87]	validation_0-logloss:0.61074
[88]	validation_0-logloss:0.61048
[89]	validation_0-logloss:0.60983
[90]	validation_0-logloss:0.60992
[91]	validation_0-logloss:0.60904
[92]	validation_0-logloss:0.60858
[93]	validation_0-logloss:0.60805
[94]	validation_0-logloss:0.60787
[95]	validation_0-logloss:0.60836
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step-04 确认最优参数

print(clf.best_params_)
1

{'base_score': 0.5, 'colsample_bylevel': 0.7, 'colsample_bynode': 0.7, 'colsample_bytree': 0.6, 'gamma': 0, 'learning_rate': 0.1, 'max_bin': 12, 'max_depth': 6, 'min_child_weight': 30, 'n_estimators': 1000, 'reg_alpha': 2, 'reg_lambda': 3, 'subsample': 0.65}
1

step-05 选取最优模型

best_model=clf.best_estimator_
1

step-06 评价最优模型

model_eval2(best_model, train.values, test.values)
1

train_roc_auc_score: 0.8766644056264636
test_roc_auc_score: 0.7278343023255814
train_accuracy_score: 0.8
test_accuracy_score: 0.6833333333333333
train_precision_score: 0.8069963811821471
test__precision_score: 0.7162921348314607
train_recall_score: 0.8479087452471483
test_recall_score: 0.7412790697674418
train_f1_score: 0.8269468479604452
test_f1_score: 0.7285714285714285
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step-07 保存并调用模型

joblib.dump(best_model ,  r'D:\Ensemble_Learning\xgboostinfo\xgboostgridbest.model')
best_model=joblib.load( r'D:\Ensemble_Learning\xgboostinfo\xgboostgridbest.model')
model_eval2(best_model, train.values, test.values)
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train_roc_auc_score: 0.8766644056264636
test_roc_auc_score: 0.7278343023255814
train_accuracy_score: 0.8
test_accuracy_score: 0.6833333333333333
train_precision_score: 0.8069963811821471
test__precision_score: 0.7162921348314607
train_recall_score: 0.8479087452471483
test_recall_score: 0.7412790697674418
train_f1_score: 0.8269468479604452
test_f1_score: 0.7285714285714285
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集成学习01_xgboost参数讲解与实战

一.xgboost 模型参数介绍

二.xgboost 两种方式实现

三. 网格搜索最优xgboost参数

一.XGBoost的参数

1 通用参数

1）booster[默认gbtree]

2）silent[默认0]

3）nthread[默认值为最大可能的线程数]

4）num_feature [set automatically by xgboost, no need to be set by user]

2 booster参数

1）eta[默认0.3]

2）min_child_weight[默认1]

3）max_depth[默认6]

4）max_leaf_nodes

5）gamma[默认0]

6）max_delta_step[默认0]

7）subsample[默认1]

8）colsample_bytree[默认1]

9）colsample_bylevel[默认1]

10）lambda[默认1]

11）alpha[默认1]

12）scale_pos_weight[默认1]

13) Parameter for Linear Booster

lambda_bias

3 学习目标参数

1）objective[默认reg:linear]

2）eval_metric[默认值取决于objective参数的取值]

3）seed(默认0)

4）sklearn 参数对照

4.平台控制参数 Console Parameters

二.xgboost 实现

1.导入相关包

2.数据处理

3.结合sklearn的xgboot模型

step01-拟合模型

step02-评价模型

step03-利用模型预测

step04-保存和调用模型

注意点

4.直接采用xgboost的模型

step-01 构建参数

step-02 处理数据

step-03 拟合模型

step-04 用模型预测

注意：

step-05 评价模型效果

step-06 保存模型并调用

三. 网格搜索最优xgboost参数

1.step-01 配置参数列表

step-02 选择待优化模型

step-03 进行网格搜索 拟合模型

step-04 确认最优参数

step-05 选取最优模型

step-06 评价最优模型

step-07 保存并调用模型

step-03 进行网格搜索拟合模型