Welcome to regression in machine learning! Regression is a fundamental supervised learning technique that helps us predict continuous numerical values. Unlike classification which predicts categories, regression predicts quantities like prices, temperatures, or ages. Here we see a simple example where we plot data points and find the best line that fits through them.
There are different types of regression models. Linear regression assumes a straight-line relationship between input and output, following the formula y equals mx plus b. This works well when data follows a linear pattern. Polynomial regression can capture curved relationships using formulas like y equals ax squared plus bx plus c. The choice depends on the underlying pattern in your data.
The training process involves several key steps. First, we collect training data with input features and target values. Next, we choose an appropriate model type. Then we fit the model to the data by finding the best parameters that minimize prediction errors. The orange dashed lines show the errors between actual and predicted values. The goal is to adjust the model until these errors are as small as possible.
To evaluate regression models, we use several key metrics. Mean Squared Error measures the average of squared differences between actual and predicted values. Root Mean Squared Error is simply the square root of MSE. Mean Absolute Error calculates the average absolute differences. R-squared indicates how well the model explains the variance in the data, with values closer to one being better. These metrics help us compare different models and choose the best one.
Regression has countless real-world applications. In real estate, we predict house prices based on size, location, and age. Stock markets use regression to analyze trends and forecast prices. Medical professionals use it to predict disease progression from symptoms. Weather services forecast temperature using atmospheric data. Businesses predict sales revenue from marketing investments. Here we see a simple example of predicting house prices based on size, where our trained model can estimate the price of a new house.