Helpful examples of using AdaBoost machine learning algorithms in scikit-learn.
The AdaBoost (Adaptive Boosting) algorithm is an ensemble learning technique primarily used for classification tasks, though it can also be adapted for regression.
AdaBoost works by combining multiple weak learners, typically decision stumps (trees with a single split), to create a strong classifier. The algorithm operates in a sequential manner, where each weak learner is trained on the data, with particular emphasis on the instances that were misclassified by previous models.
This emphasis is achieved by adjusting the weights of the training samples: initially, all samples have equal weights, but with each iteration, the weights of misclassified samples are increased, making the algorithm focus more on difficult cases.
The final model is a weighted sum of the weak learners, where each learner’s contribution is determined by its accuracy.
AdaBoost is effective at improving the performance of weak classifiers, enhancing accuracy and reducing variance. However, it can be sensitive to noisy data and outliers, as these can disproportionately influence the model.