Welcome to Unit 3: Machine Learning Model Development!
This interactive guide will help you understand how to build and evaluate ML models step by step.
🎯 Module 1: Model Selection
Predictive vs Descriptive Models
🔮 Predictive Models
- Supervised Learning
- Has target variable
- Used for prediction
- Examples: Classification, Regression
🔍 Descriptive Models
- Unsupervised Learning
- No target variable
- Used for pattern discovery
- Examples: Clustering, Association
🧩 Model Selection Helper
Step 1: Do you have labeled data?
🚀 Module 2: Training Process
Holdout Method vs K-Fold Cross Validation
📊 Holdout Method
- Single split (70-80% train, 20-30% test)
- Fast and simple
- High variance
🔄 K-Fold Cross Validation
- Multiple splits (K folds)
- More reliable
- Lower variance
- Better performance estimation
🔬 K-Fold Visualization
📈 Module 3: Model Evaluation
Confusion Matrix Calculator
| Predicted Positive | Predicted Negative | |
| Actual Positive | TP | FN |
| Actual Negative | FP | TN |
🎯 Accuracy
Overall correctness: (TP + TN) / Total
💎 Precision
Quality of positive predictions: TP / (TP + FP)
🔍 Recall
Coverage of actual positives: TP / (TP + FN)
⚖️ F1 Score
Balance between precision and recall
🔧 Module 4: Performance Improvement
Overfitting Simulator
💡 Techniques to Improve Performance
- ✅ Feature Engineering
- ✅ Hyperparameter Tuning
- ✅ Regularization
- ✅ Cross-validation
- ✅ Handle Imbalanced Data
- ✅ Collect More Data