Model Training and Evaluation

Intermediate

🛠️ Training & Evaluating a Machine Learning Model

Training a machine learning model involves:

  1. 📥 Feeding prepared data into an algorithm
  2. ⚙️ 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.