Bias-Variance Tradeoff in Machine Learning | Avoid Overfitting & Underfitting!

📌 1️⃣ What is Bias? (Underfitting Problem)
✅ Definition:
Bias occurs when a model is too simple and cannot capture the true pattern in the data.

High bias models ignore important details, leading to poor performance.

✅ How to Identify High Bias (Underfitting)?
The model performs poorly on both training and test data.

Predictions are too simplistic and miss important patterns.
📌 2️⃣ What is Variance? (Overfitting Problem)
✅ Definition:
Variance occurs when a model memorizes training data too well and fails to generalize to new data.

High variance models are too complex and capture noise instead of patterns.

✅ How to Identify High Variance (Overfitting)?
The model performs very well on training data but poorly on test data.

Predictions change drastically with small variations in input data.

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