✅ Machine Learning Basics – Interview Q&A 🤖📚

1️⃣ What is Supervised Learning?
It’s a type of ML where the model learns from labeled data (input-output pairs). Example: predicting house prices.

2️⃣ What is Unsupervised Learning?
ML where the model finds patterns in unlabeled data. Example: customer segmentation using clustering.

3️⃣ Difference: Regression vs Classification?
⦁ Regression predicts continuous values (e.g., price).
⦁ Classification predicts categories (e.g., spam or not spam).

4️⃣ What is Bias-Variance Tradeoff?
⦁ Bias: error from wrong assumptions → underfitting.
⦁ Variance: error from sensitivity to small fluctuations → overfitting.
Good models balance both.

5️⃣ What is Overfitting & Underfitting?
⦁ Overfitting: Model memorizes data → poor generalization.
⦁ Underfitting: Model too simple → can't learn patterns.
Use regularization, cross-validation, or more data to handle these.

6️⃣ What is Train-Test Split?
Splitting dataset (e.g., 80/20) to train and test model performance on unseen data.

7️⃣ What is Cross-Validation?
A technique to evaluate models using multiple train-test splits (like k-fold) for better generalization.

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