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Can we use XGBoost for multi class classification?
It is more apt for multi-class classification task. By default,XGBClassifier or many Classifier uses objective as binary but what it does internally is classifying (one vs rest) i.e. if you have 3 classes it will give result as (0 vs 1&2). If you’re dealing with more than 2 classes you should always use softmax.
Can we use XGBoost for classification?
XGBoost provides a wrapper class to allow models to be treated like classifiers or regressors in the scikit-learn framework. This means we can use the full scikit-learn library with XGBoost models. The XGBoost model for classification is called XGBClassifier.
What is the proper usage of scale _ Pos _ weight in XGBoost?
Generally, scale_pos_weight is the ratio of number of negative class to the positive class. Suppose, the dataset has 90 observations of negative class and 10 observations of positive class, then ideal value of scale_pos_weight should be 9. See the doc: http://xgboost.readthedocs.io/en/latest/parameter.html. Share. Improve this answer.
How does XGBoost deal with imbalanced classification data?
According to XGBoost documentation, the scale_pos_weight parameter is the one dealing with imbalanced classes. See, documentation here scale_pos_weight, [default=1] Control the balance of positive and negative weights, useful for unbalanced classes.
How is scale _ Pos _ weight handled in multiclass case?
I know that there is a parameter called scale_pos_weight. But how is it handled for ‘multiclass’ case, and how can I properly set it? scale_pos_weight is used for binary classification as you stated. It is a more generalized solution to handle imbalanced classes.
Is there a problem with XGBoost in R?
The problem is the dataset is heavily imbalanced with only around 1000 being in the positive class. I am trying to use xgboost (in R) for doing my prediction.