I learned about the workings of catBoost algorithm which is specifically used for classification tasks. In catBoost, we have to choose all the categorical columns, convert all the object columns into category type, because the catBoost model only accepts the columns in categorical type.

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Done

Done

The assumptions of the CatBoost algorithm are as follows:1-Categorical Features: CatBoost is designed to handle categorical features effectively, allowing for direct handling of categorical variables without explicit encoding.2-Ordinal Variables: CatBoost can handle ordinal variables, where the order or ranking of categories is meaningful.3-Missing Values: CatBoost can handle missing values in categorical features by treating them as a separate category during training.4-Feature Interactions: CatBoost automatically captures feature interactions, including interactions between numerical and categorical features.5-Outliers: CatBoost is generally robust to outliers in the target variable, although extreme outliers may still impact model performance.These assumptions and considerations allow CatBoost to leverage categorical features and capture complex relationships within the data effectively.

I have done this video with 100% practice.

I have done this lecture with 100% practice.

CatBoost algorithm is very good at dealing the categorical features