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Description
| SESSION | JUL – AUG 2024 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | III |
| COURSE CODE & NAME | DADS303 INTRODUCTION TO MACHINE LEARNING |
Assignment Set – 1
- What do you mean by Machine Learning? Discuss the relevance of Machine Learning in Business.
Ans 1.
Machine Learning and Its Relevance in Business
Machine Learning (ML) is a subset of artificial intelligence (AI) that enables machines to learn from data and make decisions or predictions without explicit programming. Unlike traditional programming, where rules are coded manually, ML uses algorithms to parse data, identify patterns, and make predictions or decisions. The essence of ML lies in its ability to improve automatically over time as it processes more data. This improvement stems from its capacity to find patterns in
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- What is Support Vector Machine? What are the various steps in using Support Vector Machine?
Ans 2.
Support Vector Machine and Its Steps
Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It is particularly effective for high-dimensional datasets and complex problems where the data is not linearly separable. SVM works by identifying the hyperplane that best separates the data
- Discuss all the assumptions of linear regression.
Ans 3.
Assumptions of Linear Regression
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The model is based on certain assumptions that, when met, ensure the reliability and validity of the results. These assumptions are foundational to understanding
Assignment Set – 2
- Explain the K-Means Clustering algorithm
Ans 4.
K-Means Clustering Algorithm
K-Means clustering is one of the most popular unsupervised machine learning algorithms used for partitioning a dataset into a predefined number of clusters (k). Unlike supervised learning, K-Means does not rely on labeled data. Instead, it groups similar data points together based on their features,
- Discuss various validation measures used for Machine Learning in detail.
Ans 5.
Various Validation Measures Used in Machine Learning
Validation measures in machine learning are crucial for evaluating the performance and effectiveness of a model. These measures ensure that the model generalizes well to unseen data, avoiding overfitting or underfitting. Below are detailed explanations of key validation measures commonly used in
- Briefly explain ‘Splitting Criteria’, ‘Merging Criteria’ and ‘Stopping Criteria’ in Decision Tree.
Ans 6.
Splitting Criteria, Merging Criteria, and Stopping Criteria in Decision Trees
Decision trees are popular supervised learning algorithms used for both classification and regression tasks. They work by recursively splitting the dataset into subsets based on feature values, ultimately creating a tree structure. The quality and efficiency of the tree depend on the splitting, merging, and stopping criteri