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Practice

Regression Model for Predicting Continuous Values

Regression models analyze input data to predict continuous numerical values. Unlike classification models that categorize input data into specific classes, regression models predict a numerical value corresponding to the given input.

For example, a regression model can predict stock prices or forecast the temperature on a given day.


Characteristics of Regression Models​

Regression model outputs are continuous and can take on a wide range of values.

For example, a model predicting temperature will not output a fixed number but rather various real numbers like 23.5°C, 24.1°C, 25.3°C.

Weather Prediction Model
Input: Weather Data → Output: 23.5°C (Predicted Temperature)

Regression predictions are not fixed classes but numerical values that can be directly interpreted.

For instance, a model predicting a student's exam score provides an estimated score like 82.7 rather than simply indicating pass or fail.


Types of Regression Models​

Regression models can be categorized based on the nature of their input and output variables.


1. Simple Regression​

This model uses a single input variable (feature) to predict a single output value.

For example, a model predicting house price based on its area falls into this category.

Simple Regression Example
Input: House Area → Output: Predicted Price

2. Multiple Regression​

This type of regression uses multiple input features to predict a single output.

For example, predicting house price based on area, number of rooms, and location is a multiple regression case.

Multiple Regression Example
Input: (Area, Number of Rooms, Location) → Output: Predicted Price

Evaluating the Performance of Regression Models​

When evaluating the performance of regression models, metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² Coefficient of Determination are used more commonly than Accuracy.

This is important to determine how close the model's predictions are to the actual values.

In the next lesson, we will delve into these evaluation metrics for regression models in detail.

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