ISRO-DEC2017-78

Question

Consider the results of a medical experiment that aims to predict whether someone is going to develop myopia based on some physical measurements and heredity. In this case, the input dataset consists of the person's medical characteristics and the target variable is binary: $1$ for those who are likely to develop myopia and $0$ for those who aren't. This can be best classified as

• A. Regression 
• B. Decision Tree
• C. Clustering
• D. Association Rules

Answer 1

Most preferred answer here is Answer B Decision trees 

Regression is generally used to predict a particular value like 2.3 , 4,3 as output ,even though logistic regression can be used to predict binary output variable .

Clustering is unsupervised learning so it is preferred for input data which are not more related and form them into groups .

Decision tree comes under supervised learning and is mostly used than regression For example GBDT is most used technique in machine learning apart from Deep learning algos

associativity is defintitely not possible .

Comments

Please provide link for these topics.

Answer 2

In this case, the best classification for the medical experiment is Decision Tree.

• Regression: Regression analyzes focus on predicting continuous variables, such as blood pressure or tumor size. Myopia, being a binary outcome (develops or doesn't develop), is not suitable for regression.
• Clustering: Clustering aims to group similar data points together without a predefined target variable. Predicting myopia development requires a prediction for each individual based on their features, making clustering less suitable.
• Association Rules: Association rules find relationships between items in a dataset. While they might identify connections between specific physical measurements and myopia, they wouldn't directly predict individual cases.
• Decision Tree: Decision trees are well-suited for classification tasks with binary outcomes. They create a tree-like structure where each node represents a decision based on a feature, and the leaves represent the predicted outcome. This aligns perfectly with your scenario of predicting myopia development based on individual characteristics.

Answer 3

option B

Comments

why can't it be clustering?