1. **What is Supervised Learning?**

Ans. Supervised learning is a type of machine learning where the algorithm learns from labeled data, making predictions or decisions based on input features.

2. **What is the difference between Regression and Classification?**

Ans. Regression predicts continuous numerical values, while classification predicts categorical labels or classes.

3. **What is Overfitting?**

Ans. Overfitting occurs when a model learns the training data too well, capturing noise and random fluctuations. It performs poorly on new, unseen data.

4. **What is a Hyperparameter?**

*Ans.* A hyperparameter is a configuration setting of a model that is set before training and remains constant during training.

5. **Give an example of a Classification Algorithm.**

*Ans.* Logistic Regression is an example of a classification algorithm, used to predict binary outcomes.

6. **Explain Bias-Variance Tradeoff.**

*Ans.* The bias-variance tradeoff is the balance between a model’s simplicity (bias) and its ability to fit diverse data patterns (variance). Finding the right balance minimizes prediction errors.

**7**. **What is Cross-Validation?**

Ans. Cross-validation is a technique used to assess the performance of a model. The dataset is divided into subsets for training and testing, allowing for more robust evaluation.

8. **Why do we use a Test Set?**

*Ans.* The test set is used to evaluate the model’s performance on data it has never seen before, providing an unbiased estimate of its predictive power.

9. **What is Feature Engineering?**

Ans. Feature engineering involves creating or transforming features from raw data to improve a model’s performance.

10. **Explain Precision in Classification.**

*Ans.* Precision is the ratio of true positive predictions to the total predicted positives. It measures the accuracy of positive predictions.

11. **Define Recall in Classification.**

*Ans.* Recall (Sensitivity) is the ratio of true positive predictions to the total actual positives. It measures the ability of the model to identify all relevant cases.

12. **What is the F1-Score?**

*Ans.* The F1-Score is the harmonic mean of precision and recall. It provides a balanced measure of a model’s performance.

13. **Explain Regularization.**

Ans. Regularization is a technique used to prevent overfitting by adding a penalty term to the model’s loss function.

14. **Name an Ensemble Learning Technique.**

*Ans.* Bagging (Bootstrap Aggregating) is an ensemble learning technique that combines the predictions of multiple base learners to improve overall performance.

15. **What is Gradient Descent?**

*Ans.* Gradient Descent is an iterative optimization algorithm used to minimize the loss function of a model by adjusting the model’s parameters in the direction of steepest descent.

16. **Define Confusion Matrix.**

*Ans.* A confusion matrix is a table that visualizes the performance of a classification algorithm, showing the true positive, true negative, false positive, and false negative counts.

17. **Explain One-Hot Encoding.**

*Ans.* One-hot encoding is a technique used to represent categorical variables as binary vectors, where only one bit is ‘hot’ (1) indicating the category.

18. **What is a Decision Tree?**

*Ans.* A decision tree is a tree-like model used for both classification and regression. It makes decisions based on the values of input features.

**19. Define K-Nearest Neighbors (KNN).**

*Ans.* K-Nearest Neighbors is a simple, instance-based learning algorithm where predictions are made by averaging the target values of ‘K’ nearest neighbors in the training set.

20. **What is a Neural Network?**

*Ans.* A neural network is a computational model inspired by the structure of the human brain. It consists of layers of interconnected nodes (neurons) and is used for various machine learning tasks.