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Supervised Learning involves training models on labeled datasets where input features correspond to known outputs. Linear Regression is a fundamental supervised learning algorithm utilized to model relationships between dependent and independent variables. This chapter covers the implementation of linear regression in Python, along with the evaluation of model performance using metrics like Mean Squared Error and R² Score.
Chapter 611 sections
Supervised Learning involves training models on labeled datasets where input features correspond to known outputs. Linear Regression is a fundamental supervised learning algorithm utilized to model relationships between dependent and independent variables. This chapter covers the implementation of linear regression in Python, along with the evaluation of model performance using metrics like Mean Squared Error and R² Score.
Supervised learning involves model training using labeled datasets.
Linear regression models the relationship between target and feature variables using a straight line.
Model performance can be evaluated using Mean Squared Error and R² Score.
A type of machine learning where a model is trained using labeled data, with input-output pairs.
A supervised learning algorithm that models the relationship between a dependent variable and one or more independent variables using a linear equation.
A metric used to evaluate regression models, computed as the average of the squares of the errors between predicted and actual values.
A statistical measure that represents the proportion of the variance for a dependent variable that's explained by independent variables in a regression model.
Total Questions
2
Estimated Time
4 min
Passing Score
70%
1 more question available