Supervised Learning
Supervised learning is a machine learning approach that uses labeled data. Each input has a known output. The model learns the link between them. After training, the model predicts outputs for new inputs.
Core Idea
The model studies examples with labels. It learns patterns. It tries to reduce errors. Then it generalizes to unseen data.
How Supervised Learning Works
- Collect data with labels.
- Split data into training and testing sets.
- Train a model on the training set.
- Measure performance on the test set.
- Use the model for real predictions.
Key Types of Supervised Learning
1. Classification
Classification predicts a class label. The output is discrete.
Examples
- Email spam or not spam
- Image category
- Sentiment detection
2. Regression
Regression predicts a numeric value. The output is continuous.
Examples
- House price
- Sales numbers
- Temperature prediction
Popular Supervised Algorithms
- Linear regression
- Logistic regression
- Decision trees
- Random forest
- Support Vector Machine
- KNN
- Neural networks
When to Use Supervised Learning
- You have labeled data.
- You need precise predictions.
- You want clear evaluation metrics.
Strengths
- Strong performance with quality labels
- Clear training process
- Easy evaluation
Limitations
- Needs labeled data
- Labeling can take time
- May not generalize well with weak data
Common Evaluation Metrics
For Classification
- Accuracy
- Precision
- Recall
- F1 score
For Regression
- MSE
- MAE
- RMSE
- R2 score
Supervised Learning in Moroccan Darija
Supervised learning howa type dial ML li kayst3mel data m3a labels. Kul input kaykoun 3ando output ma3rouf. Model kayt3llam had relation, w b3d kaydir predictions jdadin.
Kif Kaykhddam
- Kandkhlo data mlabel.
- Kandrbo model.
- Kanchoufo performance f test set.
- Kanst3mlo model f predictions.
Types
- Classification. Output class.
- Regression. Output number.
Algorithms
- Linear regression.
- Logistic regression.
- Decision trees.
- Random forest.
- SVM.
- KNN.
- Neural networks.
Conclusion
Supervised learning depends on labeled data. It predicts classes or numbers with strong accuracy. It forms a key part of modern machine learning.