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AdaBoost Vs Gradient Boosting: A Comparison Of Leading Boosting Algorithms

AdaBoost Vs Gradient Boosting: A Comparison Of Leading Boosting Algorithms

  • Here we compare two popular boosting algorithms in the field of statistical modelling and machine learning.

In recent years, ensemble learning or boosting has become one of the most promising approaches for analysing data in machine learning techniques. The method was initially proposed as ensemble methods based on the principle of generating multiple predictions and average voting among individual classifiers.

Researchers from the Institute for Medical Biometry, Germany, have identified the key reasons for the success of statistical boosting algorithms as:

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(i) The ability of the boosting algorithms to incorporate automated variable selection and model choice in the fitting process, 

(ii) The flexibility regarding the type of predictor effects that can be included in the final model and 

(iii) The stability of these algorithms in high-dimensional data with several candidate variables rather than observations, a setting where most conventional estimation algorithms for regression settings collapse.

Here, we have compared two of the popular boosting algorithms, Gradient Boosting and AdaBoost.

AdaBoost   

AdaBoost or Adaptive Boosting is the first Boosting ensemble model. The method automatically adjusts its parameters to the data based on the actual performance in the current iteration. Meaning, both the weights for re-weighting the data and the weights for the final aggregation are re-computed iteratively. 

In practice, this boosting technique is used with simple classification trees or stumps as base-learners, which resulted in improved performance compared to the classification by one tree or other single base-learner.

Gradient Boosting

Gradient Boost is a robust machine learning algorithm made up of Gradient descent and Boosting. The word ‘gradient’ implies that you can have two or more derivatives of the same function. Gradient Boosting has three main components: additive model, loss function and a weak learner. 

The technique yields a direct interpretation of boosting methods from the perspective of numerical optimisation in a function space and generalises them by allowing optimisation of an arbitrary loss function.

The Comparison

Loss Function:

The technique of Boosting uses various loss functions. In case of Adaptive Boosting or AdaBoost, it minimises the exponential loss function that can make the algorithm sensitive to the outliers. With Gradient Boosting, any differentiable loss function can be utilised. Gradient Boosting algorithm is more robust to outliers than AdaBoost.

See Also
ensemble learning

Flexibility

AdaBoost is the first designed boosting algorithm with a particular loss function. On the other hand, Gradient Boosting is a generic algorithm that assists in searching the approximate solutions to the additive modelling problem. This makes Gradient Boosting more flexible than AdaBoost.

Benefits

AdaBoost minimises loss function related to any classification error and is best used with weak learners. The method was mainly designed for binary classification problems and can be utilised to boost the performance of decision trees. Gradient Boosting is used to solve the differentiable loss function problem. The technique can be used for both classification and regression problems. 

Shortcomings

In the case of Gradient Boosting, the shortcomings of the existing weak learners can be identified by gradients and with AdaBoost, it can be identified by high-weight data points.

Wrapping Up

Though there are several differences between the two boosting methods, both the algorithms follow the same path and share similar historic roots. Both the algorithms work for boosting the performance of a simple base-learner by iteratively shifting the focus towards problematic observations that are challenging to predict. 

In the case of AdaBoost, the shifting is done by up-weighting observations that were misclassified before, while Gradient Boosting identifies the difficult observations by large residuals computed in the previous iterations.

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