Classification is a type of Supervised Learning in machine learning, where the algorithm learns from labeled data to predict which category or class a new, unlabeled data point belongs to. The goal is to assign the correct label to input data based on patterns learned from the training set.
Examples of Classifiers
Classifier: A model used for classification tasks, predicting discrete labels or categories. For example, determining whether an email is spam or not, or identifying the species of a flower based on its features. This contrasts with a Regressor (Regression), which predicts continuous values.
Choosing a Classifier Algorithm
- Data Characteristics: Some algorithms work better on structured data, while others perform better on unstructured data.
- Problem Complexity: Simple classifiers for straightforward problems, complex models for intricate tasks.
- Model Performance: Consider accuracy and speed requirements.
- Model Interpretability: Some models, like decision trees, are easier to interpret, while others, like neural networks, can be more challenging.
- Model Scalability: Large datasets need scalable models like SVM or Naive Bayes.
- Model Flexibility: Algorithms like KNN are flexible when the data distribution is unknown.
Use Cases of Classification
- Object Recognition: Classifying objects in images (e.g., identifying a cat or a dog).
- Spam Filtering: Classifying emails as either spam or legitimate.
- Medical Diagnosis: Using patient symptoms and test results to classify diseases.