XGBoost practice sklearn learning algorithm

xgboost Chen Tianqi is a great God out out big kill, I waited a long time fresh charges have not installed on the mac good, checked a variety of installation tutorials, and later found a sentence installation, another big kill anaconda, really fragrant ~

After installing the direct use, xgboost gbdt is an upgraded version, more powerful, in parallel. Two years ago basically occupied kaggle, rolling other algorithms.

import numpy as np
import random
import sklearn
import pandas as pd
from xgboost import XGBClassifier
from xgboost import plot_importance
from sklearn import metrics
from sklearn.model_selection import train_test_split
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


column_names = ['uin', 'gender', 'age', 'play_cnt', 'share_cnt', 'influence_pv', 'ds1', 'ds2', 'ds3', 'label']

data = pd.read_csv('lr_feature.csv', usecols=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], names=column_names)
print(data.head(10))

# 分训练集测试集
X_train, X_test, y_train, y_test = train_test_split(data[column_names[1:6]], data[column_names[9]],
                                                    test_size=0.25, random_state=3)


model = XGBClassifier(learning_rate=0.01,
                      n_estimators=10,           # 树的个数-10棵树建立xgboost
                      max_depth=3,               # 树的深度
                      min_child_weight=1,        # 叶子节点最小权重
                      gamma=0.,                  # 惩罚项中叶子结点个数前的参数
                      subsample=1,               # 所有样本建立决策树
                      colsample_btree=1,         # 所有特征建立决策树
                      scale_pos_weight=1,        # 解决样本个数不平衡的问题
                      random_state=27,           # 随机数
                      slient=0
                      )
model.fit(X_train, y_train)

# 预测
y_test, y_pred = y_test, model.predict(X_test)
print("Accuracy: %.4g" % metrics.accuracy_score(y_test, y_pred))
print("F1_score: %.4g" % metrics.f1_score(y_test, y_pred))
print("Recall: %.4g" % metrics.recall_score(y_test, y_pred))
y_train_proba = model.predict_proba(X_train)[:, 1]
print("AUC Score (Train): %f" % metrics.roc_auc_score(y_train, y_train_proba))
y_proba = model.predict_proba(X_test)[:, 1]
print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_proba))

operation result:

       uin  gender  age  play_cnt  share_cnt  influence_pv  ds1  ds2  ds3  label
0  1889812       2   67         2          1             0    0    2    2    0.0
1  1966339       2   69       747         92           194   15   15   30    1.0
2  1982539       2   66      1165        104            40   12   12   24    1.0
3  2131170       3   78        53        146           117    9    3   12    1.0
4  4471700       3   81         2          0             0    1    3    4    0.0
5  4921331       3   79      1634        176           178   15   15   30    1.0
6  5441180       3   68         0          4             0    0    4    4    0.0
7  6144422       2   79       109         23            25   10   14   24    1.0
8  6807020       3   72       418         54            90   11   11   22    1.0
9  7015648       3   76       144          9            15   11    7   18    1.0
Accuracy: 0.9668
F1_score: 0.97
Recall: 0.9693
AUC Score (Train): 0.989206
AUC Score (Test): 0.988982

We used wherein relatively small, and therefore only the depth of the tree is set to 3, the number is 10, the other basic parameters with default values, when compared to the number of features when the transfer is more important participants select a good set of parameters Effect Engineering characteristics are likely to do well than to spend time and energy. Scheduling details reference may be 1,2. There are many online students can see some of the blog.

References:

1. https://zhuanlan.zhihu.com/p/52501965
2. https://zhuanlan.zhihu.com/p/68864414
3. https://blog.csdn.net/sinat_20177327/article/details/81090324
4. https://blog.csdn.net/han_xiaoyang/article/details/52665396