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Content introduction
In the field of machine learning, regression prediction is an important task, which can be used to predict continuous output variables. XGBoost is a powerful machine learning algorithm that performs well in regression prediction tasks. This article will introduce how to use the vector weighted average algorithm INFO to optimize XGBoost to achieve more accurate data regression predictions.
XGBoost is an algorithm based on gradient boosting trees, which iteratively trains multiple weak learners and combines them into a strong learner. It has achieved excellent results in many machine learning competitions and has become one of the commonly used algorithms in the industry. However, XGBoost may encounter some challenges when processing large-scale data sets, such as long training time and high memory usage.
In order to solve these problems, we can use the vector weighted average algorithm INFO to optimize XGBoost. The INFO algorithm is an ensemble learning algorithm based on vector weighted average. It can weighted average the prediction results of multiple models to obtain more accurate prediction results. In XGBoost, we can use the INFO algorithm to weighted average the prediction results of multiple XGBoost models to improve the accuracy of regression predictions.
Specifically, the INFO algorithm can optimize XGBoost’s regression prediction through the following steps:
- Data set division: Divide the original data set into a training set and a test set. The training set is used to train multiple XGBoost models, while the test set is used to evaluate the performance of the models.
- Model training: Use the training set to train multiple XGBoost models. The performance of the model can be optimized by adjusting the model’s hyperparameters.
- Fusion of prediction results: For each sample in the test set, multiple XGBoost models are used for prediction, and their prediction results are weighted and averaged. Weights can be determined based on model performance and confidence.
- Performance evaluation: Use the weighted average prediction results to evaluate the performance of the model. Various regression evaluation metrics can be used, such as mean square error (MSE) and coefficient of determination (R2).
Part of the code
function Yhat = xgboost_test(p_test, model)
%% Read model
h_booster_ptr = model.h_booster_ptr;
%% Get input data related attributes
rows = uint64(size(p_test, 1));
cols = uint64(size(p_test, 2));
p_test = p_test';
%% Set necessary pointers
h_test_ptr = libpointer;
h_test_ptr_ptr = libpointer('voidPtrPtr', h_test_ptr);
test_ptr = libpointer('singlePtr', single(p_test));
calllib('xgboost', 'XGDMatrixCreateFromMat', test_ptr, rows, cols, model.missing, h_test_ptr_ptr);
%% predict
out_len_ptr = libpointer('uint64Ptr', uint64(0));
f = libpointer('singlePtr');
f_ptr = libpointer('singlePtrPtr', f);
calllib('xgboost', 'XGBoosterPredict', h_booster_ptr, h_test_ptr, int32(0), uint32(0), int32(0), out_len_ptr, f_ptr);
%% Extract predictions
n_outputs = out_len_ptr.Value;
setdatatype(f, 'singlePtr', n_outputs);
%% get the final output
Yhat = double(f.Value);
end
Run results
References
[1] Wang Jibin, Wei Dongying, Meng Wei. Prediction method of base station coverage based on XGBoost regression algorithm. CN202211074783.9[2023-10-01].
[2] Hu Er. Forecasting the supply and demand gap of Didi Chuxing based on xgboost regression algorithm [D]. Southwestern University of Finance and Economics, 2017.
[3] Li Jun, Liu Xia, Chen Weifeng, Feng Xinglong, and Wang Yongsheng. Constructing a prediction model for perioperative deep vein thrombosis risk in patients with acute multiple injuries around the knee joint based on logistic regression and XGBoost algorithm [J]. International Journal of Surgery, 2021, 048(006):371-377, cover 3.
[4] Wang Kunzhang, Jiang Shubo, Zhang Hao, et al. Regression-classification-regression life prediction model based on XGBoost[J].[2023-10-01].