xgboost+python参数介绍的简单使用

官网参数介绍（英文版）

http://xgboost.readthedocs.io/en/latest/how_to/param_tuning.html
http://xgboost.readthedocs.io/en/latest/parameter.html

中文部分翻译版

http://blog.csdn.net/zc02051126/article/details/46711047

1. xgboost的参数介绍

1. 控制过拟合
   
   • 直接控制模型的复杂度
     
     • max_depth, min_child_weight, gamma
   • 增大产生树的随机性
     
     • subsample, colsample_bytree
     • eta, num_round
2. 处理不平衡的数据集
   
   • 预测的排序（AUC）
     
     • scale_pos_weight
   • 预测可靠性
     
     • max_delta_step
3. 参数分别介绍
   
   • booster: [default=gbtree]，可选gbtree和gblinear，gbtree使用基于树的模型进行提升计算，gblinear使用线性模型进行提升计算
   • silent: [default=0], 是否打印运行时信息，0为打印
   • nthread: [默认为支持的最大线程数], 运行时的线程数
   • num_pbuffer: [自动生成，不需要用户自己设置], 预测数量，一般是输入样本数
   • num_feature: [自动生成，不需要用户自己设置], 特征维数
   • eta: [default=0.3]，取值范围[0,1]，学习率，迭代的步长比例
   • gamma: [default=0]，取值范围[0,$\infty$]，损失阈值
   • max_depth: [default=6], 取值范围[0,$\infty$]，树的最大深度
   • min_child_weight: [default=1], 取值范围[0,$\infty$]，拆分节点权重和阈值，如果节点的样本权重和小于该阈值，就不再进行拆分
   • max_delta_step: [default=0]，取值范围[0,$\infty$]，每棵树的最大权重估计，0为没有限制
   • subsample: [default=1]，取值范围(0,1]，随机选取一定比例的样本来训练树
   • colsample_bytree: [default=1]，取值范围(0,1]，选取构造树的特征比例
   • colsample_bylevel: [default=1]，取值范围(0,1]，每个层分裂的节点数
   • lambda: [default=0]，L2 正则的惩罚系数
   • alpha: [default=0]，L1 正则的惩罚系数
   • tree_method: string，[default=’auto’]，xgboost构建树的算法，‘auto’‘exact’‘approx’‘hist’
   • lambda_bias: 在偏置上的L2正则
   • sketch_eps: [default=0.03]，只在approximate greedy algorithm上使用
   • scale_pos_weight: [default=1]，用来控制正负样本的比例
   • updater: [default=’grow_colmaker,prune’]，提供模块化的方式来构建树，一般不需要由用户设置
   • refresh_leaf: [default=1]，刷新参数，如果为1，刷新叶子和树节点，否则只刷新树节点
   • process_type: [default=’default’]，提升的方式
   • grow_policy: string [default=’depthwise’]，控制新增节点的方式，‘depthwise’，分裂离根节点最近的节点，‘lossguide’，分裂损失函数变化最大的节点
   • max_leaves: [default=0]，增加的最大节点数，只和lossguide’ grow policy相关
   • max_bins: [default=256]，只和tree_method的‘hist’相关
   • objective: [default=reg:linear], 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。没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
       base_score [ default=0.5 ]
       the initial prediction score of all instances, global bias
   • eval_metric: [默认和objective相关]，校验数据所需要的评价指标，不同的目标函数将会有缺省的评价指标（rmse for regression, and error for classification, mean average precision for ranking），用户可以添加多种评价指标，对于Python用户要以list传递参数对给程序，而不是map参数list参数不会覆盖
     
     • ’eval_metric’，可选参数如下：
       
       • “rmse”: root mean square error，均方根误差
       • “logloss”: negative log-likelihood，对数似然
       • “error”: Binary classification error rate，二值误差率，计算方法为误分样本/总样本
       • “merror”: Multiclass classification error rate，多分类误差率，计算方法同上
       • “auc”: Area under the curve for ranking evaluation.
       • “ndcg”:Normalized Discounted Cumulative Gain
       • “map”:Mean average precision
       • “ndcg@n”,”map@n”: n can be assigned as an integer to cut off the top positions in the lists for evaluation.
       • “ndcg-“,”map-“,”ndcg@n-“,”map@n-“: In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding “-” in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
         training repeatively
     • seed: [default=0], 随机数的种子。缺省值为0

2. xgboost的基本使用方法

import xgboost as xgb# 在这里设置需要的参数gbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)# 传入训练集gbm = fit(train_X, train_y)# 预测predictions = gbm.predict(test_X)

Kaggle竞赛上一个例子

https://www.kaggle.com/cbrogan/xgboost-example-python/code/code

# This script shows you how to make a submission using a few# useful Python libraries.# It gets a public leaderboard score of 0.76077.# Maybe you can tweak it and do better...?import pandas as pdimport xgboost as xgbfrom sklearn.preprocessing import LabelEncoderimport numpy as np# Load the datatrain_df = pd.read_csv('../input/train.csv', header=0)test_df = pd.read_csv('../input/test.csv', header=0)# We'll impute missing values using the median for numeric columns and the most# common value for string columns.# This is based on some nice code by 'sveitser' at http://stackoverflow.com/a/25562948from sklearn.base import TransformerMixinclass DataFrameImputer(TransformerMixin):    def fit(self, X, y=None):        self.fill = pd.Series([X[c].value_counts().index[0]            if X[c].dtype == np.dtype('O') else X[c].median() for c in X],            index=X.columns)        return self    def transform(self, X, y=None):        return X.fillna(self.fill)feature_columns_to_use = ['Pclass','Sex','Age','Fare','Parch']nonnumeric_columns = ['Sex']# Join the features from train and test together before imputing missing values,# in case their distribution is slightly differentbig_X = train_df[feature_columns_to_use].append(test_df[feature_columns_to_use])big_X_imputed = DataFrameImputer().fit_transform(big_X)# XGBoost doesn't (yet) handle categorical features automatically, so we need to change# them to columns of integer values.# See http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing for more# details and optionsle = LabelEncoder()for feature in nonnumeric_columns:    big_X_imputed[feature] = le.fit_transform(big_X_imputed[feature])# Prepare the inputs for the modeltrain_X = big_X_imputed[0:train_df.shape[0]].as_matrix()test_X = big_X_imputed[train_df.shape[0]::].as_matrix()train_y = train_df['Survived']# You can experiment with many other options here, using the same .fit() and .predict()# methods; see http://scikit-learn.org# This example uses the current build of XGBoost, from https://github.com/dmlc/xgboostgbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05).fit(train_X, train_y)predictions = gbm.predict(test_X)# Kaggle needs the submission to have a certain format;# see https://www.kaggle.com/c/titanic-gettingStarted/download/gendermodel.csv# for an example of what it's supposed to look like.submission = pd.DataFrame({ 'PassengerId': test_df['PassengerId'],                            'Survived': predictions })submission.to_csv("submission.csv", index=False)