Training an XGboost for multiclass classification for animal activity based on coordinates

The data used for training is in a tabular format

The target class in this data is imbalanced and below you can see the distribution

In this notebook, you can find ways to handle this class imbalance with the following techniques:

• Choosing the right classification model: I used XGboost because it is robust to class imbalance
• Model metrics: F1 score is a better metric than accuracy to evaluate the model performance
• Stratified train-test-splits are desirable in some cases, like when you’re classifying an imbalanced dataset, a dataset with a significant difference in the number of samples that belong to distinct classes.