Enhance bias-variance trade-off explanation in machine learning interview questions by providing detailed definitions, examples, and strategies for managing the trade-off. Include a table summarizing the relationship between model complexity, bias, variance, and generalization.

Author: imohitmayank

docs/machine_learning/interview_questions.md

@@ -408,7 +408,26 @@
 
     === "Answer"
         
-        The Bias-Variance trade-off refers to the trade-off between a model's ability to fit the training data well (low bias) and its ability to generalize well to new, unseen data (low variance). A model with high bias will underfit the training data, while a model with high variance will overfit the training data.
+        The bias-variance trade-off is a key concept in machine learning that describes the balance between two types of errors affecting a model's ability to generalize:
+
+        - **Bias**: Error from overly simplistic assumptions in the model. High bias can cause underfitting—poor performance on both training and test data.
+        - **Variance**: Error from excessive sensitivity to small fluctuations in the training set. High variance can cause overfitting—good performance on training data but poor generalization to new data.
+
+        As model complexity increases, bias decreases but variance increases. The goal is to find a balance: a model complex enough to capture patterns (low bias) but not so complex that it overfits (low variance).
+
+        | Model Complexity | Bias           | Variance      | Generalization      |
+        |------------------|----------------|---------------|---------------------|
+        | Too Simple       | High (underfit)| Low           | Poor                |
+        | Too Complex      | Low            | High (overfit)| Poor                |
+        | Just Right       | Low/Moderate   | Low/Moderate  | Good                |
+
+        **Managing the trade-off:**
+
+        - Use regularization to penalize complexity and reduce overfitting.
+        - Use cross-validation to estimate model performance on unseen data.
+        - Increasing training data can help reduce variance.
+
+        **In summary:** The bias-variance trade-off is about finding the sweet spot where your model is neither too simple nor too complex, so it generalizes well to new data.
 
 !!! Question ""
     === "Question"