How to increase accuracy of model using catboost

There are several ways you can try to increase the accuracy of a model trained with CatBoost:

1. Tune the hyperparameters: CatBoost has many hyperparameters that can affect the model's performance. You can try tuning these hyperparameters using techniques such as grid search or random search to find the best combination for your data.
2.  Use more data: Increasing the size of your training dataset can often lead to improved model accuracy, as the model has more examples to learn from.
3. Use better features: The quality of your input features can have a big impact on the accuracy of your model. You can try engineering better features for your data, or using feature selection techniques to identify the most relevant and predictive features.
4. Ensemble learning: You can try using ensemble learning techniques, such as boosting or bagging, to combine the predictions of multiple CatBoost models and potentially improve accuracy.
5. Use a different model: If you have tried the above approaches and are still not getting the accuracy you desire, you may want to try using a different machine learning algorithm to see if it performs better on your data.