Classification in Machine Learning
Classification is a type of supervised learning where the goal is to predict the category or class of a given input based on labeled training data. In classification, the output is discrete, meaning the model assigns the input to one of the predefined categories.
For example, a classification model can determine whether an email is “spam” or “not spam,” or whether a tumour is “benign” or “malignant.”
How Does Classification Work?
- Data Preparation: The dataset consists of input features and corresponding class labels (e.g., an email labeled as “spam” or “not spam”).
- Model Training: A classification algorithm learns patterns and relationships from the labeled data to differentiate between classes.
- Prediction: Once trained, the model predicts the class of new, unseen data.
- Evaluation: Metrics like accuracy, precision, recall, and F1-score are used to measure the model’s performance.
Types of Classification Problems
- Binary Classification:
- Two possible output classes.
- Example: Identifying whether a transaction is “fraudulent” or “not fraudulent.”
- Multiclass Classification:
- More than two possible output classes.
- Example: Classifying handwritten digits (0–9) in digit recognition.
- Multilabel Classification:
- Assigning multiple classes to the same input.
- Example: Tagging a social media post with categories like “food,” “travel,” and “lifestyle.”
Common Classification Algorithms
| Algorithm | Description | Use Case |
|---|---|---|
| Logistic Regression | Simple and effective for binary classification | Spam email detection |
| Decision Trees | Non-linear classifier that splits data into subgroups | Customer segmentation |
| Random Forest | Ensemble method combining multiple decision trees | Fraud detection |
| Support Vector Machines | Finds the optimal boundary between classes | Image recognition |
| K-Nearest Neighbors (KNN) | Classifies based on the majority class of nearest neighbors | Handwritten digit recognition |
| Neural Networks | Complex models for high-dimensional data | Speech and image classification |
Applications of Classification
- Healthcare: Diagnosing diseases (e.g., “diabetic” or “non-diabetic”) based on medical records.
- Finance: Classifying transactions as “fraudulent” or “legitimate.”
- Retail: Predicting whether a customer will “churn” or “stay.”
- Email Filtering: Identifying emails as “spam” or “not spam.”
- Image Recognition: Classifying objects in images (e.g., “cat,” “dog,” “car”).
Challenges in Classification
Imbalanced Data: When one class dominates the dataset, the model may struggle to predict minority classes accurately.
Overfitting: Models may perform well on training data but fail to generalise to unseen data.
High-Dimensional Data: Too many features can make classification complex and computationally expensive.