Classification Models
Classification models are supervised learning algorithms that predict discrete categories or labels. They're used when the output variable is categorical, such as "spam" or "not spam" in email filtering.
What is Classification?
Classification is a supervised learning approach where the algorithm learns from labeled training data to predict discrete class labels for new, unseen instances. The goal is to identify which category or class an observation belongs to based on a training set of data containing observations with known category memberships.
Unlike regression models that predict continuous values, classification models predict discrete values or categories. These categories can be binary (two classes) or multi-class (more than two classes).
Key Characteristics
- Predicts discrete class labels or categories
- Requires labeled training data
- Can handle binary or multi-class problems
- Evaluated using metrics like accuracy, precision, recall, and F1-score
- Decision boundaries separate different classes in the feature space
Common Classification Algorithms
A statistical model that uses a logistic function to model a binary dependent variable, commonly used for binary classification problems.
A versatile machine learning algorithm that creates a flowchart-like structure for making decisions based on feature values.
A powerful classification algorithm that finds the optimal hyperplane to separate different classes with maximum margin.
An ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees.
Common Applications
Classifying emails as spam or not spam based on content, sender information, and other features.
Predicting whether a patient has a particular disease based on symptoms and test results.
Identifying objects, people, or scenes in images by classifying them into predefined categories.
Evaluation Metrics
Classification models are evaluated using different metrics than regression models. Common evaluation metrics include:
- Accuracy: The proportion of correct predictions among the total number of predictions.
- Precision: The proportion of true positive predictions among all positive predictions.
- Recall: The proportion of true positive predictions among all actual positive instances.
- F1-Score: The harmonic mean of precision and recall, providing a balance between the two.
- ROC Curve: A graphical plot that illustrates the diagnostic ability of a binary classifier system.