![]() To evaluate your predictions, there are two important metrics to be considered: variance and bias. But how accurate are your predictions? How good is your algorithm? The temperature to be predicted depends on different properties such as humidity, atmospheric pressure, air temperature and wind speed. Let’s say you’ve developed an algorithm which predicts next week ’s temperature. Linear regression is probably the most popular form of regression analysis because of its ease-of-use in predicting and forecasting.Įvaluating a Machine Learning Regression Algorithm Regression in machine learning consists of mathematical methods that allow data scientists to predict a continuous outcome (y) based on the value of one or more predictor variables (x). The target function is $f$ and this curve helps us predict whether it’s beneficial to buy or not buy. The regression function here could be represented as $Y = f(X)$, where Y would be the MPG and X would be the input features like the weight, displacement, horsepower, etc. ![]() By plotting the average MPG of each car given its features you can then use regression techniques to find the relationship of the MPG and the input features. If you wanted to predict the miles per gallon of some promising rides, how would you do it? Well, since you know the different features of the car (weight, horsepower, displacement, etc.) one possible method is regression. Imagine you ’re car shopping and have decided that gas mileage is a deciding factor in your decision to buy. ![]() It helps in establishing a relationship among the variables by estimating how one variable affects the other. It falls under supervised learning wherein the algorithm is trained with both input features and output labels. Regression analysis is a fundamental concept in the field of machine learning.
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