Model Training and Evaluation
🛠️ Training & Evaluating a Machine Learning Model
Training a machine learning model involves:
- 📥 Feeding prepared data into an algorithm
- ⚙️ Adjusting parameters to minimize error (loss)
📉 Techniques like gradient descent help optimize this process.
📏 Evaluation Metrics
Metrics vary based on the task:
📊 Regression
- 📐 Mean Squared Error (MSE)
- 📈 R-squared
🧮 Classification
- ✅ Accuracy
- 🎯 Precision
- ♻️ Recall
- 🧮 F1 Score
- 📊 ROC-AUC
🧩 Preventing Overfitting
To ensure the model generalizes well, use:
- 🔁 Cross-validation
- ➖ Regularization (L1, L2)
- ✂️ Pruning
📚 Cross-validation partitions data into training and validation sets to test model robustness.
🔄 Example: K-Fold Cross-Validation
- The dataset is split into k parts
- The model is trained and tested on different combinations
- ✅ Ensures fairness and robustness
🧑🤝🧑 Analogy: Like rotating team members to ensure balanced performance across all roles.