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Pruning In Decision Tree ?
Pruning In Decision Tree (1) What Is Pruning? Pruning is a technique used in decision trees to reduce overfitting and improve the generalization ability of the model. It involves removing branches or nodes from the tree that do not contribute significantly to its predictive accuracy. Pruning helps simplify the tree structure, making it less complex and easier to interpret. There are two main types of pruning techniques: (2) Types Of Pruning. Pre Pruning: Pre-pruning involves stopping the growth of the decision tree before it becomes fully expanded. It applies stopping criteria during the construction process to determine when to stop
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When To Stop Decision Tree Splitting?
When To Stop Decision Tree Splitting? Determining when to stop the splitting process in a decision tree is crucial to prevent overfitting or excessive complexity. Here are some common stopping criteria used in decision tree algorithms: Maximum Depth: The decision tree is limited to a maximum depth or number of levels. Once the tree reaches this depth, no further splitting is performed. Limiting the depth helps control the complexity of the tree and prevents overfitting, particularly when dealing with noisy or small datasets. Minimum Number of Samples per Leaf: Nodes are not allowed to split further if the number of
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CART, C4.5, ID3 Algorithms
CART, C4.5, ID3 Algorithms CART: CART (Classification and Regression Trees). CART is a versatile algorithm that can be used for both classification and regression tasks. It constructs binary decision trees, where each internal node represents a splitting criterion on a feature, and each leaf node represents a class label or a regression value. The splitting criterion in CART is determined by optimizing a cost function, such as the Gini index for classification or the mean squared error for regression. The algorithm recursively partitions the data based on the selected feature and splits, creating branches until a stopping condition is met.
