![]() ![]() How does the random forest algorithm work? A model like this will have high training accuracy but will not generalize well to other datasets. This means that the model is overly complex and has high variance. One of the biggest drawbacks of the decision tree algorithm is that it is prone to overfitting. Even a small change in the training dataset can make a huge difference in the logic of decision trees.Decision trees are prone to overfitting.They can partition data that isn’t linearly separable.They can be used for classification and regression problems.Decision trees are simple and easy to interpret.Now that you understand how decision trees work, let’s take a look at some advantages and disadvantages of the algorithm. Pros and Cons of the Decision Tree Algorithm: ![]()
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