What is boosting?

Data Science, AI and ML
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#1

What is boosting?

#2

Boosting is an supervised machine learning algorithm used for classification and regression problems. It is an ensemble technique which uses multiple weak learners to produce a strong model for regression and classification.

ref:https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d
ref:https://medium.com/analytics-vidhya/introduction-to-the-gradient-boosting-algorithm-c25c653f826b

#3

Gradient Boosting is about taking a model that by itself is a weak predictive model and combining that model with other models of the same type to produce a more accurate model. The idea is to compute a sequence of simple decisions trees, where each successive tree is built for the prediction residuals of the preceding tree. In gradient boosting the weak model is called a weak learner. The term used when combining various machine learning models is an ensemble. The weak learner in XGBoost is a decision tree. Therefore, we need to understand how a decision tree works before we can understand boosting.