Classification

Classification is a type of supervised learning where the goal is to predict a categorical label for a given input. The model learns from labeled training data to classify new, unseen data into predefined categories.

Common use cases for classification include:

• Spam Detection: Classifying emails as "spam" or "not spam".
• Image Recognition: Identifying objects in images, such as "cat" vs "dog".
• Customer Churn Prediction: Predicting whether a customer will "churn" or "not churn".
• Credit Scoring: Classifying loan applicants as "good" or "bad" credit risks.

Main algorithms used for classification tasks include:

• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting
• Support Vector Machines