xgboost predict method returns the same predicted value for all rows
...One solution would be to increase the representation/coverage of rare target levels (e.g. non-zero insurance claims) in each tree leaf, by increasing the hyperparameter controlling minimum leaf size to some rather large values, such as those specified in the example above....
https://stackoverflow.com/questions/33470477/xgboost-predict-method-returns-the-same-predicted-value-for-all-rows
Tree Methods ? xgboost 3.0.2 documentation - Read the Docs
...XGBoost has 3 builtin tree methods, namely exact, approx and hist. Along with these tree methods, there are also some free standing updaters including refresh, prune and sync....
https://xgboost.readthedocs.io/en/stable/treemethod.html
model can't be reproduced when data is big with tree_method ... - GitHub
...I have been running into the same issue of non-reproducible result with python xgboost when I run the 'tree_method' : 'gpu_hist'. I have set the np.random.seed () just before I run the xgb.cv and passed the 'seed' in the parameter too in the xgb.cv....
https://github.com/dmlc/xgboost/issues/3921
Tune XGBoost "tree_method" Parameter | XGBoosting
...The tree_method parameter in XGBoost controls the algorithm used for constructing trees during the training process. Choosing the right tree_method can impact both the training speed and the model?s performance....
https://xgboosting.com/tune-xgboost-tree_method-parameter/
machine learning - Each time the result of XGBoost is different - Data ...
...You are missing some seeds there: I will say that StratifiedKFold (shuffle=True) without a seed can make your results change. Make sure you have both a fixed random state in the train_test_split and in the initialization of the classifier....
https://datascience.stackexchange.com/questions/68539/each-time-the-result-of-xgboost-is-different
Specifying tree_method param for XGBoost in Python
...According to the XGBoost parameter documentation, this is because the default for tree_method is "auto". The "auto" setting is data-dependent: for "small-to-medium" data, it will use the "exact" approach and for "very-large" datasets, it will use "approximate"....
https://stackoverflow.com/questions/44058786/specifying-tree-method-param-for-xgboost-in-python
Passing `updater` parameter produces warning about ignored `tree_method ...
...If one passes parameter updater, xgboost will throw a warning about tree_method being ignored, even if said parameter is not passed. The warning occurs in both python and R, and happens only the first time that xgb.train with said parame......
https://github.com/dmlc/xgboost/issues/9964
Configure XGBoost "tree_method" Parameter | XGBoosting
...Choosing the appropriate ?tree_method? parameter in XGBoost is crucial for optimizing both the speed of training and the performance of the model, especially when dealing with large datasets. This tip explores how to select the best tree construction algorithm based on your data size and computational resources....
https://xgboosting.com/configure-xgboost-tree_method-parameter/
Why XGBoost can?t solve all your problems. - Towards Data Science
...Due to the method with which tree-based models partition the input space of any given problem, these algorithms are largely unable to extrapolate target values beyond the limits of the training data when making predictions....
https://towardsdatascience.com/why-xgboost-cant-solve-all-your-problems-b5003a62d12a/
Tree Methods ? xgboost 1.7.6 documentation - Read the Docs
...XGBoost has 4 builtin tree methods, namely exact, approx, hist and gpu_hist. Along with these tree methods, there are also some free standing updaters including refresh, prune and sync. The parameter updater is more primitive than tree_method as the latter is just a pre-configuration of the former....
https://xgboost.readthedocs.io/en/release_1.7.0/treemethod.html