AI engineering glossary · Classical ML algorithms
What is adaBoost?
AdaBoost: Adaptive Boosting: a boosting algorithm that trains weak learners in turn, raising the weight of rows that were misclassified so the next learner focuses on them. Final predictions are a weighted vote in which more accurate learners count for more.
AdaBoost: an example
With 1,000 rows, each starts at weight 0.001; after round one, wrongly classified rows might rise to 0.003 while correct ones fall.
Related terms
More Classical ML algorithms terms
- Agglomerative clustering
- Anomaly detection
- Apriori algorithm
- Association rule learning
- Bagging
- Bayesian network
- Bernoulli naive Bayes
- Boosting
Where you learn it
AdaBoost is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.