Let me explain ordered boosting used in the CatBoost model. In traditional boosting algorithms,when building the model, each new tree model uses the entire dataset, including the target values. This can lead to overfitting as the model May memorize the training data rather than generalize from it. In CatBoost, multiple permutations of the data set are created and they are used to build the model each instance only uses the information from previous instances in the permutation, and the final model Aggregates the results of these multiple permutations. This process is called ordered boosting, which prevents overfitting, thus helping it generalize better. CatBoost creates multiple permutations of the data set for training. Let's assume that we generate two permutations from the original dataset. The first permutation consists of (1, 2, 3), (2, 3, 4), (3, 4, 5), and (4, 5, 6). In this first permutation, the model is trained as follows: the first data point 1 2 3 is used to create the initial model. The second data point (2, 3, 4) is used to update the model based on the information from the first data point. The third data point (3, 4, 5) is used to update the model based on the information from the previous two data points. Finally the last data point (4, 5, 6) is used to update the model based on the information from all the previous data points. The second permutation is trained in the same manner. After that, the results of the models trained on each permutation are combined to build the final model.