Overfitting and Underfitting

🎯 Overfitting vs Underfitting in Machine Learning

Understanding these two common problems is key to building accurate, generalizable models.

1. 📈 Overfitting

🔍 What is it?

When a model learns too much from the training data, including noise and outliers, and fails to generalize to new, unseen data.

⚠️ Signs of Overfitting:

• Very high accuracy on training data
• Poor performance on validation/test data

📊 Example:

Imagine fitting a complex curve to a small scatterplot — it matches every point perfectly, but it’s useless for future predictions.

🛠️ How to Fix:

• Use simpler models
• Apply regularization (L1/L2)
• Prune decision trees
• Use more training data
• Apply dropout (in neural networks)
• Use cross-validation

2. 📉 Underfitting

🔍 What is it?

When a model is too simple to learn the underlying patterns in the data — it performs poorly on both training and test sets.

⚠️ Signs of Underfitting:

• Low accuracy on both training and validation data
• Model doesn’t improve even with more data

📊 Example:

Fitting a straight line to data that clearly follows a curve — the model can’t capture the pattern.

🛠️ How to Fix:

• Use a more complex model
• Add more features (feature engineering)
• Reduce regularization
• Train for longer (more epochs in neural networks)

🧠 Quick Comparison Table:

📌 Pro Tip: Use the Bias-Variance Tradeoff to find the sweet spot!

• High bias = underfitting
• High variance = overfitting
• Ideal models strike a balance.

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