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In the paragraph following equation (1): $T$ is the number of leaves in the tree. $\gamma$ is a hyperparameter that affects how much regularization occurs on the size (number of leaves) of the tree. Now it turns out that you can interpret $\gamma$ (at least roughly, see note at bottom) as ([source]): Minimum loss reduction required to make a further ...


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Xgboost is an ensemble algorithm based on decision trees, so doesn't need normalization. You can check this on Xgboost official github: Is Normalization necessary? and this post What are the implications of scaling the features to xgboost? I'm new in this algorithm but I'm pretty sure of what I've written


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Given the domain is a digital card game, deck value and game play strategy can be simulated. The wide space of options can filled in with synthetic data. Classic machine learning algorithms, such as logistic regression and boosted trees, will have limited success given the sequential nature of the problem. It would be more useful to frame it as reinforcement ...


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See this article for a bit more detail on how to better explain EFB. Here is a brief visual explanation from there with my own edits. I hope you can appreciate the high production quality of my updated graphic... To answer your main question see "Part 1 of EFB". This explains that features are ordered by their sparsity and mixed in with all other ...


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According to the XGBClassifier parameters (link) some operations will be happens on top of randomness, like subsample feature_selector etc. If we didn't set seed for random value everything different value will be chosen and different result we will get. (Not abrupt change is expected). So to reproduce the same result, it is a best practice to set the seed ...


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Look at the 'max_features' parameter in the GradientBoosting Classifier in scikit learn, here: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier That means that the algorithm is fitting different trees to try and account for the residual information after each ...


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library(tidymodels) xgboost_set <- param_set(list(learn_rate(range = c(0.01,0.3), trans = NULL), trees(range = c(200,1000), trans = NULL), #trees(): The number of trees contained in a random forest or boosted ensemble. In the latter case, this is equal to the number of boosting iterations ...


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Try to give sklearn wrapper of xgboost to sklearn OneVsRestClassifier. https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn If it doesn't work you can create four different label arrays in which samples belong to corresponding class labeled as 1s, and others as 0. Then training with each of the label arrays you can get ...


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The answer depends on the library you are using. If you are using scikit - you wouldn't need to one hot encode the targets. Scikit handles it automatically. If you were using keras to build a neural network, you might want to use one hot encoded labels because the built in loss function in keras (e.g categorical crossentropy) expects labels to be one hot ...


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dials from Tidymodels has a grid_latin_hypercube function you can use for this https://dials.tidymodels.org/reference/grid_max_entropy.html


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