13 May Types of Classification in Machine Learning
In Machine Learning (ML), classification is a supervised learning task where the goal is to predict discrete class labels (categories) for given input data. Classification can be categorized into different types based on the nature of the problem, and each type may involve different algorithms.
Types of Classification in Machine Learning
- Binary Classification
- Predicts one of two possible classes (e.g., Yes/No, Spam/Not Spam, Fraud/Not Fraud).
- Example Algorithms: Logistic Regression, Support Vector Machines (SVM), Decision Trees.
- Multi-Class Classification
- Predicts more than two classes, where each instance belongs to exactly one class.
- Example Algorithms: Random Forest, Naive Bayes, k-Nearest Neighbors (k-NN), Neural Networks.
- Multi-Label Classification
- A single instance can belong to multiple classes simultaneously.
- Example: A movie can be tagged as both “Action” and “Comedy”.
- Example Algorithms: Binary Relevance, Classifier Chains, Deep Learning models.
- Imbalanced Classification
- Deals with datasets where one class is significantly underrepresented compared to others.
- Example: Fraud detection (fraud cases are rare).
- Techniques: SMOTE (Synthetic Minority Oversampling), Cost-Sensitive Learning.
- Hierarchical Classification
- Classes are structured in a hierarchy (parent-child relationships).
- Example: Organizing products into categories and subcategories.
- Approach: Uses tree-based classifiers or recursive methods.
Are These Types the Same as Algorithms
- No, the types of classification refer to the nature of the problem (e.g., binary vs. multi-class).
- Algorithms (like Logistic Regression, SVM, Decision Trees) are the methods used to solve these classification problems.
- A single algorithm (e.g., Random Forest) can often handle multiple types of classification (binary, multi-class, etc.) with modifications.
Let us summarize:
| Classification Type | Description | Example Algorithms |
|---|---|---|
| Binary Classification | Two possible classes | Logistic Regression, SVM |
| Multi-Class Classification | More than two mutually exclusive classes | Random Forest, k-NN |
| Multi-Label Classification | Single instance can have multiple labels | Binary Relevance, Deep Learning |
| Imbalanced Classification | One class is rare | SMOTE, Cost-Sensitive Learning |
| Hierarchical Classification | Classes in a hierarchy | Tree-based classifiers |
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