CatBoost vs Other Boosting Algorithms

Quiz

One of the most effective methods for training on structural (tabular) data nowadays is the booster algorithm. The following three well known uses of boosting algorithms have provided various approaches to win machine learning competitions: CatBoost, XGBoost and LightGBM.

The main topics of this chapter will be CatBoost how well it performs compared to other algorithms and when it should be used over other methods.

The foundation of all three of the algorithms for evaluation (CatBoost, XGBoost, and LightGBM) is gradient boosting techniques. It will be useful as we proceed to have a good grasp of gradient boosting. Two varieties of gradient boosting algorithms that predict continuous and categorical variables, respectively, are classifiers and regressions.

This method, in addition to Adaptive Boosting (Adaboost), includes training learners based upon reducing the differential loss function of a weak learner using a gradient descent optimization procedure. So each student receives an equal distribution of the weights. Gradient boosting uses decision trees connected in series as weak learners.

Because of its sequential construction in which decision trees are added one at a time without affecting the ones that already exist, it is a stage-wise additive model.

Reducing the bias error of the model is the primary objective of gradient boosting. Because of its greedy algorithm, the algorithm can overfit a training dataset as per the bias variance tradeoff very quickly. However, to reduce this over-fitting, regularization, shrinkage, tree limitation, and random gradient boosting can all be applied.

Overview of CatBoost

Similar to XGBoost and LightGBM rankers, CatBoost also features a ranking mode called CatBoostRanking, but CatBoost offers many more powerful versions than XGBoost and LightGBM. The differences consist of −

Ranking (YetiRank, YetiRankPairwise)
Pairwise (PairLogit, PairLogitPairwise)
Ranking + Classification (QueryCrossEntropy)
Ranking + Regression (QueryRMSE)
Select top 1 candidate (QuerySoftMax)

Additionally, CatBoost offers ranking benchmarks which compare several ranking variations of CatBoost, XGBoost, and LightGBM, such as −

CatBoost: RMSE, QueryRMSE, PairLogit, PairLogitPairwise, YetiRank, YetiRankPairwise
XGBoost: reg:linear, xgb-lmart-ndcg, xgb-pairwise
LightGBM: lgb-rmse, lgb-pairwise

CatBoost Parameters

Although XGBoost and CatBoost use the same training parameters, it has a far more flexible parameter adjustment interface. The table below provides a quick comparison of the parameters offered by the three boosting methods.

Function	CatBoost	XGBoost	LightGBM
Parameters controlling over-fitting	learning_rate, depth, l2_reg	learning_rate, max_depth, min_child_weight	learning_rate, Max_depth, Num_leaves, min_data_in_leaf
Parameters for handling categorical values	cat_features, one_hot_max_size	N/A	Categorical_feature
Parameters for controlling speed	rsm, iteration	colsample_bytree, subsample, n_estimators	feature_fraction, bagging fraction, num_iterations

CatBoost vs XGBoost vs LightGBM

This section will compare algorithms, features, performance and speed, parameters for over-fitting control, speed control parameters and community support. So check the below table to see the differences −

Factors	CatBoost	XGBoost	LightGBM
Algorithm Basics	Designed for categorical data, CatBoost natively handles categorical features without needing to preprocess them.	Focuses on gradient boosting and handles missing data efficiently. Known for its accuracy and speed in competitions.	Specializes in large datasets and faster training by using histogram-based methods, making it efficient for large and sparse datasets.
Handling Categorical Features	Strong advantage as it can handle categorical features directly using cat_features without manual conversion.	Does not handle categorical features natively, requiring conversion to numerical format like one-hot encoding.	Supports categorical features with the Categorical_feature parameter but not as efficient as CatBoost.
Speed and Performance	Generally slower than LightGBM, but optimized for both speed and accuracy when dealing with categorical data.	Slower compared to LightGBM but provides more stable and accurate results across diverse datasets.	Known for its speed, especially with large datasets. It trains faster due to its leaf-wise growth strategy.
Parameters for Overfitting Control	Uses parameters like learning_rate, depth, and l2_reg to avoid overfitting.	Controls overfitting with learning_rate, max_depth, and min_child_weight.	Uses learning_rate, Max_depth, Num_leaves, and min_data_in_leaf for managing overfitting with precise tree control.
Speed Control Parameters	Parameters like rsm and iteration help control the speed of model training.	Speed can be adjusted using colsample_bytree, subsample, and n_estimators.	Controlled with feature_fraction, bagging_fraction, and num_iterations for speed tuning.
Community Support	Popular but with a smaller community compared to XGBoost and LightGBM.	Very popular with extensive community support, tutorials, and pre-built models.	Highly popular for speed in large-scale tasks, with a strong user community.
Conclusion	Best for handling categorical features directly without preprocessing.	Ideal for general-purpose tasks where accuracy is key and training time isn't a major concern.	Great for large datasets, especially where fast training is needed with scalable performance.

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