xgboost | 易学教程

XGBoost Best Iteration

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问题 I am running a regression using the XGBoost Algorithm as, clf = XGBRegressor(eval_set = [(X_train, y_train), (X_val, y_val)], early_stopping_rounds = 10, n_estimators = 10, verbose = 50) clf.fit(X_train, y_train, verbose=False) print("Best Iteration: {}".format(clf.booster().best_iteration)) It correctly trains itself, but the print function raises the following error, TypeError: 'str' object is not callable How can I get the number of the best iteration of the model? Furthermore, how can I

Is the xgboost documentation wrong ? (early stopping rounds and best and last iteration)

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问题 here below is a question about xgboost early stopping rounds parameter and how it does, or does not, give the best iteration when it is the reason why the fit ends. In xgboost documentation, one can see in the scikit learn api section (link) that when the fit stops due to the early stopping rounds parameter: Activates early stopping. Validation error needs to decrease at least every "early_stopping_rounds" round(s) to continue training. Requires at least one item in evals. If there’s more

How to extract decision rules (features splits) from xgboost model in python3?

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问题 I need to extract the decision rules from my fitted xgboost model in python. I use 0.6a2 version of xgboost library and my python version is 3.5.2. My ultimate goal is to use those splits to bin variables ( according to the splits). I did not come across any property of the model for this version which can give me splits. plot_tree is giving me something similar. However it is visualization of the tree. I need something like https://stackoverflow.com/a/39772170/4559070 for xgboost model 回答1:

XGBoost 引入 - 提升树

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认识提升树这个boosting 跟 Adaboost 不同. Adaboost 是通过上一轮的误差率来动态给定一下轮样本不同的权重来学习不同的模型. 现在的方式, 更多是基于残差的方式来训练. 一个比较现实的栗子是, 我训练了一个模型 m1, 但发现, 效果不怎么好, 得到的残差比较大. 但是呢, 我又不想将 m1 废弃掉. 如何才能改进呢? 这是一个非常现实的问题. 解决的方案可以是, 基于m1 的基础上再做训练...就这样, 通过一个个的模型进行训练, 下一个模型来弥补上一个模型的"不足" 就以决策树而言, 提升树的模型可以写为: $f_M(x) = \sum \limits _{m}^M T(x, \Theta_m)$ M 表示树的棵树 T( ) 表示决策树, $\Theta$ 是 "\Theta" 它的基本思想是, 通过已知前一棵树的情况下, 来决定下一棵树的形状 $f_m(x) = f_{m-1}(x) + T(x, \Theta_m)$ 而参数的求解: $\hat \Theta = arg \ min _{\Theta_m} \sum \limits _{i=1}^N L(y_i, f_{m-1}(x) + T(x_i; \Theta_m))$ 以回归问题为栗子,当损失函数用误差的平方来衡量时: \(L(y, f(x)) = (y-f(x))

How is xgboost quality calculated?

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问题 Could someone explain how the Quality column in the xgboost R package is calculated in the xgb.model.dt.tree function? In the documentation it says that Quality "is the gain related to the split in this specific node". When you run the following code, given in the xgboost documentation for this function, Quality for node 0 of tree 0 is 4000.53, yet I calculate the Gain as 2002.848 data(agaricus.train, package='xgboost') train <- agarics.train X = train$data y = train$label bst <- xgboost(data

XGBoostLibraryNotFound: Cannot find XGBoost Library in the candidate path, did you install compilers and run build.sh in root path?

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问题 I am facing this problem while moving the python-package directory of XGBoost. Traceback (most recent call last): File "setup.py", line 19, in LIB_PATH = libpath'find_lib_path' File "xgboost/libpath.py", line 46, in find_lib_path 'List of candidates:\n' + ('\n'.join(dll_path))) builtin.XGBoostLibraryNotFound: Cannot find XGBoost Library in the candidate path, did you install compilers and run build.sh in root path? Could anyone explain to me how to fix it? thanks in advance. 回答1: You get that

implement XGboost custom objective function

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问题 I am trying to implement a custom objective function using XGboost (in R but I also use python so any feedback about python is also good). I created a function that spit back gradient and hessian (it works properly), but when I try to run xgb.train then it is not working. I then decided to print for each round the predictions, gradient and hessian in this specific order. This is the output (it keeps repeating as long as I let it run): [1] 0 0 0 0 0 0 0 0 0 0 [1] -0.034106908 -0.017049339 -0

Install xgboost under python with 32-bit msys failing

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问题 Trying to install xgboost is failing..? The version is Anaconda 2.1.0 (64-bit) on Windows & enterprise. How do I proceed? I have been using R it seems its quite easy to install new package in R from RStudio, but not so in spyder as I need to go to a command-window to do it and then in this case it fails.. import sys print (sys.version) 2.7.8 |Anaconda 2.1.0 (64-bit)| (default, Jul 2 2014, 15:12:11) [MSC v.1500 64 bit (AMD64)] C:\anaconda\Lib\site-packages>pip install -U xgboost Downloading

error happens when install xgboost4.0 in windows7, python2.7

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问题 Here is the process I tried to install xgboost: git clone --recursive https://github.com/dmlc/xgboost git submodule init git submodule update cp make/mingw64.mk config.mk It was good until I ran the code in my git bash make -j4 It goes wrong: F:/mingw64/x86_64-w64-mingw32/include/stdio.h:450:83: error: 'FILE* std::fopen(const char*, const char*)' should have been declared inside 'std' FILE *fopen64(const char * __restrict__ filename,const char * __restrict__ mode); ^ F:/mingw64/x86_64-w64

Xgboost Prediction is different for C++ and Python for the same model

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问题 I've trained a model in python using the following code(I didnt use a testing set for this example, I was training and predicting using the same dataset, to make the illustration of the problem easier): params = {'learning_rate':0.1,'obj':'binary:logistic','n_estimators':250, 'scale_pos_weight':0.2, 'max_depth' : 15, 'min_weight' : 1, 'colsample_bytree' : 1, 'gamma' : 0.1, 'subsample':0.95} X = np.array(trainingData,dtype = np.uint32) #training data was generated from a csv X = xgb.DMatrix(np