Classification models are used when the output variable is categorical. These models assign data into predefined classes or categories.
For example, an email spam filter classifies emails as spam or not spam. Similarly, a medical diagnosis model may classify patients as healthy or diseased.
Logistic Regression is a commonly used classification algorithm. Despite its name, it is used for classification tasks. Decision Trees and Random Forests are also widely used because they are easy to interpret.
Classification models work by learning decision boundaries that separate different classes. These boundaries help the model assign new data points to the correct category.
Evaluation metrics such as accuracy, precision, recall, and F1-score are used to measure performance. In critical applications like healthcare, precision and recall are more important than accuracy.
Challenges in classification include handling imbalanced datasets where one class is much more frequent than others. Techniques like resampling and class weighting are used to address this issue.
Classification models play a crucial role in real-world applications and are one of the most widely used machine learning techniques.
