In this session, we learnt the intuition behind boosting and studied in detail the AdaBoost algorithm.
- Adaboost starts with a uniform distribution of weights over training examples.
- These weights tell the importance of the data point being considered.
- We first start with a weak learner h1(x) to create the initial prediction.
- Patterns which are not captured by previous models become the goal for the next model by giving more weightage.
- The next model(weak learner) trains on this resampled data to create the next prediction.
- In the end, we make a weighted sum of all the linear classifiers to make a strong classifier.
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