In this video tutorial, we've explained Decision Trees in great detail. You'll also learn the math behind splitting the nodes. The next video will show you how to code a decision tree classifier from scratch.
Decision tree classification is a supervised learning algorithm that uses a tree-like structure to predict a categorical value. The tree is built by recursively splitting the data into smaller and smaller subsets until the desired level of accuracy is achieved. At each split, the algorithm chooses the feature that best splits the data into two groups with the most similar values. The predicted class for a new instance is then determined by following the path down the tree until a leaf node is reached.
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