Classification Model and Regression Model in AI

In this exercise, we will learn about the Artificial Intelligence and its related terms.

Classification Model Definition A classification model is a type of machine learning algorithm used to predict discrete class labels based on input features. It maps input data to a set of predefined categories or classes.
Key Characteristics

Common Algorithms

Applications

Regression Model Definition A regression model is a type of machine learning algorithm used to predict continuous numerical values. It learns the relationship between the dependent variable (target) and independent variables (features).
Key Characteristics

Examples:

  1. Predicting house prices based on square footage, location, and number of bedrooms.
  2. Estimating the number of sales a store will make next month.
  3. Forecasting stock market trends.

Common Algorithms

Applications

Note: In a regression model, both the features (independent variables) and the target (dependent variable) should typically be numerical.
If we have categorical data, it must be converted into a numerical format using encoding techniques like:
* One-hot encoding: Creates binary columns for each category.
* Label encoding: Assigns a unique integer to each category.
If the target is categorical (e.g., labels or classes), a classification model should be used instead.