Chapter 6: Supervised Learning – Linear Regression
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.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
-
6.9.1Summary
What we have learnt
- 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.
Key Concepts
- -- Supervised Learning
- A type of machine learning where a model is trained using labeled data, with input-output pairs.
- -- Linear Regression
- A supervised learning algorithm that models the relationship between a dependent variable and one or more independent variables using a linear equation.
- -- Mean Squared Error (MSE)
- A metric used to evaluate regression models, computed as the average of the squares of the errors between predicted and actual values.
- -- R² Score
- A statistical measure that represents the proportion of the variance for a dependent variable that's explained by independent variables in a regression model.
Additional Learning Materials
Supplementary resources to enhance your learning experience.