Classification is a supervised machine learning task where the goal is to predict the category or class of a given input based on historical labeled data.
Classification Model Workflow
1 Data Collection: Gather relevant and labeled data.
2 Data Preprocessing: Clean and transform data into a suitable format.
3 Feature Selection: Choose the most important variables.
4 Model Training: Use algorithms to learn patterns in the data.
5 Evaluation: Test the model on unseen data to measure accuracy.
6 Prediction: Use the trained model to classify new data points.
Popular Classification Algorithms
1 Decision Trees: Intuitive models that split data into smaller subsets.
2 Random Forest: An ensemble of decision trees for improved accuracy.
3 K-Nearest Neighbors (KNN): Classifies based on neighboring data points.
4 Naive Bayes: Suitable for text classification (e.g., spam filtering).
5 Neural Networks: Used for complex, large-scale classification tasks.
Evaluation Metrics for Classification Models
- Accuracy: The percentage of correct predictions.
- Precision: The proportion of true positives among predicted positives.
- Recall (Sensitivity): The proportion of true positives correctly identified.
- F1 Score: A balance between precision and recall.
Real-World Applications of Classification
Healthcare
- Predicting diseases like cancer or diabetes.
- Classifying medical images (e.g., MRI scans),
Finance
- Fraud detection in credit card transactions,
- Assessing loan application risk.
Email Services
- Spam filtering.
- Categorizing emails into folders (e.g., Promotions, Updates).