Financial Risk Manager Part 1
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Which of the following best describes the role of linear regression and logistic regression in machine learning?
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TTanishq
A
Linear regression is used to predict continuous variables, while logistic regression is used to predict categorical variables.
B
Linear regression is used for unsupervised learning, while logistic regression is used for supervised learning.
C
Linear regression is used for classification, while logistic regression is used for regression.
D
Both linear regression and logistic regression can be used for both classification and regression.
Explanation:
Linear regression is used to predict continuous variables, while logistic regression is used to predict categorical variables. Linear regression is a type of regression analysis that is used to predict a continuous target variable based on one or more input features. Logistic regression, on the other hand, is a classification algorithm that is used to predict a binary outcome (such as "yes" or "no") based on one or more input features.
Why other options are incorrect:
- B is incorrect: Both linear and logistic regressions are supervised machine learning techniques.
- C is incorrect: Linear regression is instead used for regression, while logistic regression is used for classification.
- D is incorrect: Linear regression cannot be used for classification. When using a linear regression model for binary classification, where the dependent variable Y can only be 0 or 1, it is possible for the model to predict probabilities outside of the range of 0 to 1. This occurs because the model is attempting to fit a straight line to the data, and the predicted values may not be restricted to the valid range of probabilities. As a result, the model may produce predictions that are less than zero or greater than one.
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