Weak Classifier
In machine learning, a weak classifier is a simple model that performs slightly better than random guessing. It is often used as a building block for more complex models in ensemble learning. Weak classifiers are trained on a subset of the data and assign a weight to each data point based on how well it is classified. The weights of misclassified data points are increased, while the weights of correctly classified data points are decreased. This process is repeated multiple times, with each iteration focusing on the misclassified data points from the previous iteration. The final model is a weighted combination of all the weak classifiers, with the weights determined by their performance on the training data.
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