◑ applying · kind algorithm · level 3 · 16h
- Generaliza: Decision Trees
Bagged decision trees with feature subsampling — averages away variance. A robust default that rarely overfits catastrophically.
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of Decision Trees during training. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the average of the predictions of the trees. Random forests correct for Decision Trees’ habit of overfitting to their training set.
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- Generaliza: Decision Trees