Consider the following statements w.r.t Gradient Boosting and choose the correct: At each iteration, we add an incremental model, which is fitted on the negative gradients of the loss function w.r.t the previous predictions. We multiply λ t (learning rate) with the incremental model h t + 1 so that the new model doesn't overfit.
Answers
Answer: We multiply λ t (learning rate) with the incremental model h t + 1 so that the new model doesn't overfit is the correct option.
Given :
- At each iteration, we add an incremental model, which is fitted on the negative gradients of the loss function w.r.t the previous predictions.
- We multiply λ t (learning rate) with the incremental model h t + 1 so that the new model doesn't overfit.
To Find : Consider the following statements w.r.t Gradient Boosting and choose the correct.
Explanation:
Concept : Gradient boosting - Gradient boosting is a boosting method, and it stands out for its prediction speed and accuracy, particularly with large and complex datasets. There are mainly two types of error in any dataset - bias error and variance error. Gradient boosting algorithm helps us to minimize the bias error of the model. The principle behind boosting algorithms is at first we built a model on the training dataset, then a second model is built to minimize the errors present in the first model. When the target column are continuous in problem dataset, we use Gradient Boosting Regressor and when it is a classification problem, we use Gradient Boosting Classifier. The only difference between the two boosting type is the “Loss function”. The goal here is to minimize the loss function by adding weak learners using gradient descent.
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