Next, I will explain ordered Target encoding used in CatBoost. Ordered target encoding is a method used in CatBoost to handle categorical features during the training process. This technique captures the relationship between categorical features and the target variable while reducing the risk of target leakage. To briefly explain, target leakage refers to a premature exposure of the target during the training process. When converting categorical values to their mean target values, there is a risk that target information may leak into the training data. If the target values are included in the training data, it may lead to high accuracy on the training data but poor predictions on the test data. Sure, let's explain Ordered Target Encoding and CatBoost with an example where there are three categories of color (red, green, yellow) and a binary target variable. The data set with the encoded 'color' feature would look like this. In ordered target encoding the categorical variable is replaced by the mean of the corresponding target values. For the first data point, the global mean of the target values is used. For subsequent data points, the mean of the previous target values for that category is used. For example, the first 'red' value is replaced by the global mean of 0.6. The second 'red' is replaced by the mean of the previous red value, which is 1. The third 'red' is replaced by the mean of the previous two 'red' values, both of which have target values of 1, the mean is 1. Similarly, the first green value is replaced by the global mean of 0.6. The second green is replaced by the mean of the previous 'green' value which is 0. In this way, the categorical data is encoded into numerical form. Let's take a look at one of the features of the CatBoost model called Categorical Feature Combination. Categorical Feature Combination is one of the particularly useful features in CatBoost. It refers to the technique of automatically creating new features by combining existing categorical features. This is done to capture the interactions between categorical features, better represent the data, and improve the model's performance. The process does not necessarily involve identifying redundant categorical features to merge them into a single variable. Instead, it focuses on creating additional features that represent combinations of the original categorical features. For example, if you have two categorical features, 'color' and 'size', CatBoost can combine these two features to create a new feature. This new feature such as 'color_size' assigns values to each unique combination of 'color' and 'size'. These combined features can enhance the models's performance.