Linear regression is a supervised machine-learning algorithm that is primarily used for predictive analysis. It is one of the top machine learning algorithms that interviewers ask if you’re coming from the Data Science or Machine Learning field or targeting similar job roles.
The term “linear regression” refers to a procedure that displays a linear relationship between one or more independent (y) variables and a dependent (y) variable.
Given that it displays a linear connection, linear regression determines how the value of the dependent variable varies in response to the value of the independent variable. The goal is to train an algorithm to anticipate accurate data by receiving input.
What is Linear Regression in Machine Learning
Linear Regression was developed in the field of statistics but has been borrowed and widely embraced by Machine Learning for several reasons, including simplicity, versatility, and efficiency. Linear regression is regarded as a baseline model, which means if it brings satisfactory results, there may be little justification for using it in more complex scenarios.
Questions around Linear regression algorithms are likely to be asked for Applied Scientists, Research Scientists, Data Scientists, and Machine Learning Engineering positions.
The most common questions asked in the interviews are how Linear Regression works and explain it with a working example. The other most common Linear Regression question is to define the four assumptions of Linear Regression algorithm. Aspirants are expected to break down the solution to the problem statement in the interviews.
What is the goal of Linear Regression?
The main goal of a linear regression is to accurately predict the outcome based on the dependent and independent variables. Note that the variable which is used to predict the other variable’s value is called the independent variable.
When there is a strong, straight-line relationship between variables, the predictions made by the model tend to be more accurate.
Linear-regression statistical algorithm is adopted by Machine Learning because it’s a proven way to scientifically and reliably predict the future. It helps to make better and more informed decisions.
Linear regression takes the massive data that organizations have collected to better manage the prediction.
Four Assumptions of Linear Regression
Linear regression relies on four key assumptions to ensure the validity of its results:
Linearity
The relationship between the independent variable(s) and the dependent variable should be linear. This means that a change in the independent variable(s) should correspond to a proportional change in the dependent variable.
Example: Suppose we want to predict house prices based on their size. We assume that as the size of the house increases, its price also increases proportionally. If the relationship between house size and price is not linear (e.g., the price increases exponentially with size), linear regression may not be appropriate.
Homoscedasticity
The variance of the residuals (the differences between the observed and predicted values) should be constant across all levels of the independent variable(s). This means that the spread of the residuals should be consistent across the range of predicted values.
Example: In a regression predicting exam scores based on study hours, homoscedasticity implies that the variability in exam scores is consistent regardless of the number of study hours. If the spread of exam scores widens or narrows as study hours increase, the homoscedasticity assumption is violated.
Independence
The observations should be independent of each other. In other words, the value of one observation should not be influenced by the value of another observation.
Normality of Residuals
The residuals should be normally distributed. This means that the distribution of the residuals (the differences between the observed and predicted values) should follow a bell-shaped curve centered around zero.
Linear Regression in Machine Learning Working Example
Watch the video to understand the working example of Linear Regression. Watch our instructor who’s a Machine Learning Engineer at Visa coding Linear Regression from scratch! Understand when to use linear regression and when not to use.
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Linear regression stands as a fundamental yet profound technique within the realm of machine learning. Its ability to predict and interpret continues to make it an invaluable tool across various applications – from finance and healthcare to technology and beyond.
Understanding its workings and examples helps demystify much of the complexity surrounding machine learning, creating a bridge between theoretical concepts and real-world application.
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Linear Regression in Machine Learning FAQs
â€How does Linear Regression work?
Linear regression works by finding the best-fit line (or hyperplane in higher dimensions) that minimizes the difference between the predicted values and the actual observed values in the training data.
It does this by adjusting the coefficients of the linear equation to minimize the cost function, such as Mean Squared Error (MSE).
What are the applications of Linear Regression?
Linear regression has various applications across different domains, including:
- Predicting house prices based on features like size, number of bedrooms, and location.
- Forecasting sales based on advertising expenditure, market trends, and other factors.
- Analyzing the relationship between independent variables and outcomes in scientific research.
- Predicting stock prices based on historical data and market indicators.
- Estimating the impact of factors like age, gender, and lifestyle on health outcomes.
What’s the difference between Simple Linear Regression and Multiple Linear Regression?
Simple linear regression involves predicting a dependent variable using a single independent variable, while multiple linear regression involves predicting the dependent variable using two or more independent variables.
In other words, simple linear regression fits a straight line to the data, while multiple linear regression fits a hyperplane.
