Machine Learning

Explain the concept of decision trees in classification. Provide an example of building and visualizing a decision tree using R. How can K-means clustering be applied to a dataset in R?

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Introduction Decision trees and K-means clustering are two popular techniques in data science and machine learning. While decision trees are mainly used for classification problems, K-means clustering is an unsupervised learning method used to group data based on similarity. In this blog post, we will explain the concept of decision trees, how to implement and […]

Explain the concept of decision trees in classification. Provide an example of building and visualizing a decision tree using R. How can K-means clustering be applied to a dataset in R? Read More »

MCS-224: Artificial Intelligence and Machine Learning – Complete Assignment Answers (2024-25)

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MCS-224: Artificial Intelligence and Machine Learning Programme Code: MAEC Course Code: MCS 224 Assignment Code: MCS 224 / AST/2024-2025 Total Marks: 100 Assignment Questions and Answer Links: Classify AI on the basis of the functionalities of AI. Also discuss some important applications of AI. Define Supervised, Unsupervised and Reinforcement learning with suitable examples of each

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Q16: Explain FP Tree Growth Algorithm with a suitable example

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Introduction FP-Growth (Frequent Pattern Growth) is an efficient and scalable method for mining frequent itemsets without the need for candidate generation, unlike the Apriori algorithm. It uses a data structure called the FP-Tree (Frequent Pattern Tree). Steps of the FP-Growth Algorithm Scan the transaction database to determine the frequency (support count) of each item. Remove

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Q15: Compute the Linear Discriminant projection for the following two-dimensional dataset: X1 =(x1, x2) = (4, 2), (2, 2), (3, 2), (3, 5), (3, 4) X2 = (x1, x2) = (8, 7), (9, 6), (7, 7), (9, 8), (10, 9)

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Introduction Linear Discriminant Analysis (LDA) is a supervised learning algorithm used for classification and dimensionality reduction. The goal is to project high-dimensional data onto a lower-dimensional space such that the separability between classes is maximized. Step-by-Step LDA Calculation for the Given Dataset Step 1: Define the datasets Class X1: (4, 2), (2, 2), (3, 2),

Q15: Compute the Linear Discriminant projection for the following two-dimensional dataset: X1 =(x1, x2) = (4, 2), (2, 2), (3, 2), (3, 5), (3, 4) X2 = (x1, x2) = (8, 7), (9, 6), (7, 7), (9, 8), (10, 9) Read More »

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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Q9: Briefly discuss the various Ensemble methods.

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Introduction Ensemble methods in machine learning are techniques that combine the predictions from multiple models to improve accuracy and performance. Instead of relying on a single model, ensemble learning leverages the power of many to produce more reliable results. These methods are particularly effective in reducing variance, bias, and improving predictions. Major Types of Ensemble

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Compare Artificial Intelligence, Machine Learning, and Deep Learning

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Introduction Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are closely related terms but they are not the same. Each concept builds upon the previous one, with AI being the broadest, ML being a subset of AI, and DL being a further subset of ML. Understanding the differences and relationships among these terms

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