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Bagging, Boosting & Stacking Technique.
Bagging, Boosting & Stacking Technique Introduction: Bagging and boosting are two ensemble learning techniques commonly used in machine learning. Both approaches aim to improve the predictive performance of individual models by combining multiple models together. However, they differ in how they construct and combine the models. (1) Bagging Technique:(Bootstrap Aggregating): Bagging involves creating multiple copies of the original training dataset through a technique called bootstrapping. Bootstrapping randomly samples the training data with replacement, resulting in different subsets of data for each model. Each model in the ensemble is trained independently on one of the bootstrapped datasets. Bagging typically uses majority
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Random Forest Algorithm
Random Forest Algorithm Table Of Contents: What Is Random Forest Algorithm? Working Principle Of Random Forest Algorithm. Essential Features Of Random Forest. Important Hyperparameters In Random Forest Algorithm. Difference Between Random Forest And Decision Tree. Advantages and Disadvantages Of Random Forest Algorithm. (1) What Is Random Forest Algorithm? The Random Forest algorithm is an ensemble learning method that combines multiple decision trees to create a robust and accurate predictive model. Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. (2) How Random Forest Algorithm Works? Step-1: Ensemble of Decision Trees: Random Forest builds an ensemble of
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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.
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Entropy Vs. Gini Index
Entropy Vs. Gini Index (1) Difference In Entropy & Gini Index. Gini Index: It is the probability of misclassifying a randomly chosen element in a set. The range of the Gini index is [0, 1], where 0 indicates perfect purity and 1 indicates maximum impurity. The Gini index is a linear measure. It can be interpreted as the expected error rate in a classifier. It is sensitive to the distribution of classes in a set. The computational complexity of the Gini index is O(c). It is less robust than entropy. It is sensitive. Formula for the Gini index is Gini(P)
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Gini Index In Decision Tree.
GINI Index In Decision Tree Table Of Contents: What Is the GINI Index? Interpreting GINI Index. Example Of GINI Index. What Is the GINI Coefficient? (1) What Is GINI Index? The Gini index, also known as the Gini impurity, is a measure used in decision tree algorithms to quantify the impurity or randomness of a set of examples within a particular node. It is an alternative criterion, alongside entropy, for determining the best feature to split the data. The Gini index is calculated based on the distribution of class labels within a node. It measures the probability of incorrectly classifying
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Information Gain In Decision Tree.
Information Gain Table Of Contents: What Is Information Gain? Example Of Information Gain. (1) What Is Information Gain? Information gain is a measure used in decision tree algorithms to determine the best feature to split the data. It quantifies how much information a particular feature contributes to reducing the entropy or impurity within a node. The information gain is calculated by comparing the entropy of the parent node (before the split) with the weighted average of the entropies of the child nodes (after the split), considering each possible outcome of the feature being evaluated. A higher information gain indicates that
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Entropy In Decision Tree .
Entropy In Decision Tree Table Of Contents: What Is Entropy? Interpreting Entropy. Formula For Entropy. How Decision Tree Uses Entropy? (1) What Is Entropy? Entropy is a concept used in the context of decision trees to measure the impurity or randomness of a set of examples within a particular node. In Decision Tree algorithms, Entropy is used as a criterion to determine the best feature for splitting the data. In the context of decision trees, entropy is calculated based on the distribution of class labels within a node. If a node contains only examples from a single class, it is
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Decision Tree Algorithm.
Decision Tree Algorithm! Table Of Contents: Decision Tree Algorithm. Why Use Decision Trees? Types Of Decision Trees. Terminology Related To Decision Tree. Examples Of Decision Tree. Assumptions While Creating Decision Tree. How Decision Trees Work. Attribute Selection Measures. Advantages & Disadvantages Of Decision Tree. (1) What Is Decision Tree Algorithm? A decision tree is a non-parametric supervised learning algorithm for classification and regression tasks. It has a hierarchical tree structure consisting of a root node, branches, internal nodes, and leaf nodes. Decision trees are used for classification and regression tasks, providing easy-to-understand models. In a Decision tree, there are two nodes,
