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
- Output: A class label (e.g., "Yes"/"No", "Dog"/"Cat"/"Bird").
- Task Type: Predicts categorical variables.
- Goal: To assign the input data to one of the predefined classes.
Common Algorithms
- Logistic Regression: A statistical method for binary classification.
- Decision Trees: Splits data into subsets based on feature values.
- Random Forest: An ensemble of decision trees.
Applications
- Fraud detection in transactions.
- Diagnosing diseases (e.g., cancer detection).
- Sentiment analysis of text (e.g., positive, negative, neutral).
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
- Output: A continuous value (e.g., house price, stock price, temperature).
- Task Type: Predicts numerical variables.
- Goal: To predict a value as accurately as possible.
Examples:
- Predicting house prices based on square footage, location, and number of bedrooms.
- Estimating the number of sales a store will make next month.
- Forecasting stock market trends.
Common Algorithms
- Linear Regression: Models the relationship as a straight line (linear function).
- Decision Trees and Random Forest: Can also handle regression tasks.
- Neural Networks: Useful for complex and non-linear problems.
Applications
- Predicting weather conditions like temperature or rainfall.
- Estimating life expectancy based on demographic data.
- Predicting real estate prices.
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.