Catboost Parameters and Hyperparameters

CatBoost Parameters

The model’s internal settings that it learned during training are known as the parameters. For instance, the split points and leaf values in a decision tree are parameters. You may modify a number of CatBoost’s parameters to make the training process unique. Let’s examine several crucial CatBoost settings and their functions:

• iterations: This parameter specifies the number of boosting iterations (trees) to be used during training.
• learning_rate: It controls the step size at each iteration while moving toward a minimum of the loss function.
• depth: Determines the maximum depth of the individual decision trees in the ensemble.
• l2_leaf_reg: Regularization term that prevents overfitting by penalizing large parameter values.
• cat_features: An array of indices indicating which features are categorical. CatBoost automatically handles categorical features, but you can provide additional guidance with this parameter.
• loss_function: Specifies the loss function to be optimized during training. For regression tasks, you might use ‘RMSE,’ while for classification, ‘Logloss’ is common.

CatBoost Hyperparameters

Hyperparameters: As a machine learning practitioner, you must provide these parameters before to training. They have control over a number of training-related variables, including decision tree depth and learning rate. For the model to perform well, suitable hyperparameter selection is essential.

For configuring and fine-tuning hyperparameters, CatBoost offers a versatile interface that may be broken down into many categories:

• Common hyperparameters: These are the basic hyperparameters that are applicable to any machine learning problem, such as the loss function, the learning rate, or the random seed.
• Bootstrap hyperparameters: These are the hyperparameters that control the sampling of the data for each tree, such as the bootstrap type or the subsample rate.
• Tree structure hyperparameters: These are the hyperparameters that control the shape and size of each tree, such as the depth, the number of leaves, or the minimum samples in a leaf.
• Feature importance hyperparameters: These are the hyperparameters that control how features are selected and split for each tree, such as the feature border type or the random strength.
• Regularization hyperparameters: These are the hyperparameters that control how much complexity is penalized in the model, such as the L2 regularization or the leaf estimation method.
• Overfitting detector hyperparameters: These are the hyperparameters that control how to stop the training when overfitting occurs, such as the eval metric or the use best model option.

Some of the common hyperparameters used for tuning are as follows:

• Learning rate: This feature reduces the gradient step. The longer the training process will take overall, the fewer iterations are needed the smaller the value.
• Tree Depth: Each decision tree in the ensemble has a maximum depth that is specified by the depth. Deeper trees can catch more complicated patterns, but if the threshold is set too high, they might overfit.
• Bagging temperature: It regulates how randomly samples are chosen for training. Samples become more deterministic when the value is higher (> 1), while they become random when the value is smaller (for example, 1), which may help generalization.
• Border count: It controls the most splits that are permitted for numerical features, which affects model complexity and training efficiency. Lower values speed up training but could restrict modeling, while higher values capture finer patterns but increase processing.
• L2 regularization: It adds a penalty term to the loss function during training to discourage high weight values and encourage a more basic model, aiding in the prevention of overfitting. Higher values impose stronger regularization, which is controlled by the “reg_lambda” hyperparameter.

Hyperparameter Tuning

The process of selecting the ideal collection of hyperparameters for a certain issue and dataset is known as hyperparameter tuning. For hyperparameter tuning, a variety of techniques and tools are available, including grid search, random search, Bayesian optimization, and Optuna. The general procedures for tweaking hyperparameters are:

• Define a search space: This is a range or a list of possible values for each hyperparameter that you want to tune.
• Define an objective function: This is a function that evaluates how well a model performs on a validation set with a given set of hyperparameters.
• Define a search strategy: This is a method that decides how to explore the search space and find the optimal set of hyperparameters.
• Run the search: This is where you execute the search strategy and collect the results.
• Analyze the results: This is where you compare and visualize the performance of different sets of hyperparameters and choose the best one.

For gradient boosting on decision trees, CatBoost is a well-liked open-source toolkit. It was created by Yandex and may be applied to a range of machine-learning issues, including classification, regression, ranking, and more. Compared to other boosting libraries, CatBoost has a number of benefits, including:

• It can handle categorical features automatically, without the need for encoding or preprocessing.
• It can reduce overfitting by using a novel gradient-boosting scheme and regularization techniques.
• It can achieve high performance and scalability by using efficient implementations for CPU and GPU.

In this post, we will concentrate on the CatBoost parameters and hyperparameters, which are the variables that regulate the algorithm’s operation and performance. We will describe them, how they impact the model, and how to fine-tune them for the best outcomes.