CatBoost - Boosting Process

CatBoost (short for "Categorical Boosting") is just like other gradient boosting methods like XGBoost or LightGBM, but it has some notable benefits, mainly when working with categorical data.

Key Steps in CatBoost Boosting Process

So let us discuss the key features of CatBoost boosting process −

• Data Preparation: CatBoost automatically converts categorical features into numerical values using target statistics. This maximizes the efficiency of the datasets with a high number of categorical variables.
• Model Initialization: A basic model, usually the average of the regression target variable, is the first step in the CatBoost process.
• Gradient Calculation: At every step, this method finds the gradient of the loss function. Loss function is the difference between the expected and actual values. This gradient gives the starting point for building the decision tree.
• Decision Tree Construction: CatBoost uses a symmetric tree structure in which each level of the trees has the same amount of nodes. This way we can speed up the process and improves forecast time.
• Ordered Boosting: One of unique characteristics of CatBoost is ordered boosting. Conventional boosting methods have the risk of overfitting on the training set as they calculates errors with the help of the entire dataset. But CatBoost do this by using a technique that lowers the possibility of overfitting by only using a subset of data.
• Model Updating: The predictions of the previous trees are contributed to the new tree when it is added to the ensemble, updating the predictions it creates.
• Repeat: The process is repeated until the model's performance on the validation dataset stops improving, or for the pre-specified number of iterations.

Benefits of CatBoost Boosting Process

Here are the advantages of CatBoost boosting process you should know while working with it −

• Effective Management of Categorical Features: Unlike previous methods, CatBoost does not need one-hot encoding or extensive feature engineering for handling categorical features.

• Better Performance: CatBoost generally works better on many types of data mainly when there are a lot of categories like colors or names. This is because it uses ordered boosting and other helpful methods.

Summary

CatBoost builds decision trees step by step to make better predictions. It is very good with data that has categories, like colors or names. It uses special methods like ordered boosting and target encoding to stop the model from making mistakes by learning too much from the training data. This helps the model work well with new data.