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|MASTER OF BUSINESS ADMINISTRATION (MBA)
|COURSE CODE & NAME
|DADS303 INTRODUCTION TO MACHINE LEARNING
Assignment Set – 1
- Discuss all the assumptions of linear regression 10
Introduction to Linear Regression
Linear regression is a fundamental statistical and machine learning technique used to model relationships between a dependent variable and one or more independent variables. It’s widely used due to its simplicity, interpretability, and a range of applications in different fields. Understanding the assumptions behind linear regression is crucial for its correct application and interpretation of results.
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- What do you mean by Machine Learning? Discuss the relevance of Machine Learning in Business
Machine Learning (ML) is a branch of artificial intelligence that focuses on building systems capable of learning from data, identifying patterns, and making decisions with minimal human intervention. This technology has emerged as a crucial element in the modern business landscape, revolutionizing how companies operate, make decisions, and interact with customers.
- What is Support Vector Machine? What are the various steps in using Support Vector Machine? 2 + 8
Introduction to SVM
Support Vector Machine (SVM) is a powerful and versatile supervised machine learning algorithm used for both classification and regression tasks. It is particularly well-suited for complex but small- or medium-sized datasets. The primary objective of SVM is to find a hyperplane in an N-dimensional space (N being the number of features) that distinctly classifies the data points.
Key Concept of SVM
Assignment Set – 2
- Briefly explain ‘Splitting Criteria’, ‘Merging Criteria’ and ‘Stopping Criteria’ in Decision Tree.
Splitting Criteria in Decision Trees
Splitting criteria are fundamental in the construction of decision trees, a popular tool in machine learning for tasks like classification and regression. These criteria determine how the data at a node is split into two or more homogenous sets. The goal is to find the best split that maximizes the homogeneity
- Explain the K-Means Clustering algorithm
K-Means Clustering is a fundamental algorithm in the field of machine learning, particularly in the domain of unsupervised learning. This algorithm is pivotal for data analysis, where the objective is to categorize a given dataset into clusters based on feature similarities.
- Discuss various validation measures in detail. 10
Introduction to Validation Measures in Machine Learning
In the field of machine learning, validation measures are critical for evaluating the performance of models. These measures help in understanding how well a model generalizes to new, unseen data. They are essential for both choosing the right model for a given task and for tuning