Machine Learning
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AdaBoost
Quick Definition
Combining weak classifiers with adaptive sample weighting
Full Definition
Adaptive Boosting, an ensemble algorithm combining weak classifiers by assigning higher weights to misclassified samples.
Examples
face detection, binary classification, sequential ensemble
Related Terms
boosting
ensemble-learning
decision-tree
More Machine Learning Terms
Model Selection
Choosing the best ML model from candidates
XGBoost
Optimized gradient boosting library for structured data
Feature Engineering
Creating and transforming input variables to improve performance
Markov Chain
Stochastic model where events depend only on previous state
UMAP
Fast dimensionality reduction preserving local and global structure
Lasso Regression
Regularized regression using L1 penalty for feature selection