Machine Learning, Uc Berkeley, Random Forest, Decision Tree, Ensemble Learning.
data we can use to train each classifier becomes quite limited. Replacing the data point every time we sample allows us to maintain the distribution of our data, and reduces the amount that our final classifiers’ predictions vary from input to input due to the fact that each training subset is