Classification

Q13: Explain K-Nearest Neighbors classification Algorithm with a suitable example.

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Introduction K-Nearest Neighbors (KNN) is a simple and intuitive supervised machine learning algorithm used for classification and regression tasks. It classifies new data points based on the majority label of the ‘k’ closest training examples in the feature space. How KNN Works Choose the number of neighbors ‘k’ Calculate the distance (e.g., Euclidean) between the […]

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Q12: Explain Naïve Bayes Classification Algorithm with a suitable example.

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Introduction Naïve Bayes is a simple yet powerful probabilistic classification algorithm based on Bayes’ Theorem. It assumes that the features are conditionally independent given the class label. Despite its ‘naïve’ assumption, it performs well in various real-world scenarios like spam detection, sentiment analysis, and document classification. Bayes’ Theorem Bayes’ Theorem is the foundation of the

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Q11: Explain Decision Tree algorithm with the help of a suitable example.

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Introduction Decision Tree is a popular supervised learning algorithm used for both classification and regression tasks. It uses a tree-like structure where each internal node represents a test on a feature, each branch represents an outcome of the test, and each leaf node represents a class label or output. Key Concepts Root Node: The starting

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Q10: What is logistic regression? Explain with the help of a suitable example.

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Introduction Logistic Regression is a type of supervised learning algorithm used for classification problems. Unlike linear regression which predicts continuous values, logistic regression is used to estimate the probability that a given input belongs to a certain category. What is Logistic Regression? Logistic regression is used when the dependent variable is categorical (usually binary —

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