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Naive Bayes Algorithm
Naive Bayes Algorithm Table Of Contents: What Is Naive Bayes Algorithm? What Is Conditional Probability? What Is Bayes Theorem? Why Is It Called Naive Bayes? Assumptions Of Naive Bayes Algorithm. What Is Bayesian Probability? How Does The Naive Bayes Algorithm Works? Types Of Naive Bayes Model. Pros & Cons Of Naive Bayes Algorithm. Applications Of Naive Bayes Algorithm. (1) What Is Naive Bayes Algorithm? The Naive Bayes algorithm is a probabilistic machine learning algorithm commonly used for classification tasks. It is based on Bayes’ theorem, which describes the probability of an event given prior knowledge or evidence. The “naive” assumption
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K – Nearest Neighbors Algorithm
K – Nearest Neighbors Table Of Contents: What Is K – Nearest Neighbor Algorithm? How Does The KNN Algorithm Work? How Do We Choose The Factore K? Distance Metrics Used In KNN Algorithm. Advantages & Disadvantages Of KNN Algorithm. Applications Of KNN Algorithm. (1) What Is K – Nearest Neighbor? The k-nearest neighbours (k-NN) algorithm is a popular supervised machine learning algorithm used for both classification and regression tasks. It operates based on the principle that data points with similar features tend to belong to the same class or have similar output values. It is widely disposable in real-life scenarios
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Support Vector Machine
Support Vector Machine Table Of Contents: What Is a Support Vector Machine? How Does Support Vector Machine Work? Types Of Support Vector Machine Algorithms. Mathematical Intuition Behind Support Vector Machine. Margin In Support Vector Machine. Optimization Function and Its Constraints. Soft Margin SVM. Kernels In Support Vector Machine. How To Choose A Right Kernel? (1) What Is Support Vector Machine? The Support Vector Machine (SVM) algorithm is a supervised machine learning algorithm used for classification and regression tasks. It is particularly effective in solving binary classification problems but can also be extended to multi-class classification. SVMs can be used for
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Weak Learner vs. Strong Learner.
Weak Learner Vs. Strong Learner Table Of Contents: Introduction. Weak Learner. Strong Learner. Conclusion. (1) Introduction: In machine learning, the terms “strong learner” and “weak learner” refer to the performance and complexity of predictive models within an ensemble or learning algorithm. These terms are often used in the context of boosting algorithms. (2) Weak Learner: A weak learner is a model that performs slightly better than random guessing or has limited predictive power on its own. Weak learners are typically simple and have low complexity, such as decision stumps (a decision tree with only one split), shallow decision trees, or
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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)
