Is there any implementation of XGBoost algorithm for decision trees in Matlab?

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Roberto
Roberto 2018-10-13
回答： Ali Ebrahimzade 2024-6-4
I've found other boosting algos available in fitensemble and fitcensemble options but not XGBoost. Any chance to find it somewhere else? Thanks
Roberto
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Bernhard Suhm
Bernhard Suhm 2020-9-4
As stated in the article Michelle referred you to, XGBoost is not an algorithm, just an efficient implementation of gradient boosting in Python. MATLAB supports gradient boosting, and since R2019b we also support the binning that makes XGBoost very efficient. You activate the binning with the NumBins name-value parameter to the fit*ensemble functions.

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Jeffrey van Prehn
Jeffrey van Prehn 2020-5-23
Please see: https://nl.mathworks.com/matlabcentral/fileexchange/75898-functions-to-run-xgboost-in-matlab (2 functions to train and test xgboost models). The examples are for classification, but xgboost can also be used for regression. The functions are wrappers for the xgboost.dll library.
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Srishti Gaur
Srishti Gaur 2022-7-12
Hi Roberson
Here is the error:
Error using movefile
No matching files named 'C:\Post_doc_research\XG_boost\lib\tmp\xgboost\lib\xgboost.dll' were found.
Error in xgboost_install (line 32)
movefile(from, to);
How can I get xgboost.dll file?
Please help me out with this.

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Redha Almahdi
Redha Almahdi 2018-10-19
Hi Roberto,
I am looking for XGBoost matlab based implementation as well. PLease if you get any let me know.
Thanks
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Walter Roberson
Walter Roberson 2018-10-20
编辑：Walter Roberson 2019-4-18
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563301/ talks about preprocessing in MATLAB and about using Python scikit libraries for xgboost. It does not actually state that they call Python from MATLAB but that approach would sound plausible.

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Ali Ebrahimzade
Ali Ebrahimzade 2024-6-4
%% Load Dataset
data = readtable('dataset.csv');
X = data(:,1:end-1); % Input features
y = data(:,end); % Target variable (electrical/thermal efficiency)
%% Split Data into Train and Test
cv = cvpartition(numel(y),'HoldOut',0.2); % 20% for testing
X_train = X(cv.training,:);
y_train = y(cv.training,:);
X_test = X(cv.test,:);
y_test = y(cv.test,:);
%% XGBoost Model
model = XGBTreeBagger('Trees', 200, 'MinLeafSize', 3, 'OOBPrediction','On');
model = fitcensemble(model, X_train, y_train);
y_pred_train = oobPredict(model);
y_pred_test = predict(model, X_test);
%% Extra Trees Model
model = TreeBagger('NumTrees',200,'OOBPredictorImportance','On');
model = fitcensemble(model, X_train, y_train);
y_pred_train = oobPredict(model);
y_pred_test = predict(model, X_test);
%% KNN Model
mdl = fitrknn(X_train,y_train,'NumNeighbors',5);
y_pred_train = predict(mdl,X_train);
y_pred_test = predict(mdl,X_test);
%% Performance Evaluation
R2_train = rsquared(y_train,y_pred_train)
R2_test = rsquared(y_test,y_pred_test)

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