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Machine Learning Interview Questions and Answers

Test your skills through the online practice test: Machine Learning Quiz Online Practice Test

Ques 1. What is the difference between supervised and unsupervised learning?

Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data where the algorithm tries to find patterns or relationships on its own.

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Ques 2. Explain the bias-variance tradeoff in machine learning.

The bias-variance tradeoff is a key concept in model selection. High bias leads to underfitting, while high variance leads to overfitting. It's about finding the right balance to achieve optimal model performance.

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Ques 3. What is cross-validation, and why is it important?

Cross-validation is a technique used to assess the performance of a model by dividing the dataset into multiple subsets, training the model on some, and testing on others. It helps to obtain a more reliable estimate of a model's performance.

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Ques 4. Explain the concept of feature engineering.

Feature engineering involves transforming raw data into a format that is more suitable for modeling. It includes tasks like scaling, normalization, and creating new features to improve the performance of machine learning models.

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Ques 5. What is overfitting, and how can it be prevented?

Overfitting occurs when a model learns the training data too well, capturing noise and producing poor generalization on new data. Regularization techniques, cross-validation, and increasing training data are common methods to prevent overfitting.

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