Must-Know Questions

Cracking Data Science Interviews

How can you prevent overfitting in a machine-learning model?

1

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Describe the process of cross-validation. Why is it useful?

2

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How will you handle missing values in a dataset?

3

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What is cross-validation? And why is it used?

4

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How will you evaluate the performance of classification models?

5

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What criteria would you use to choose the most relevant features in a machine learning project?

6

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What metrics & techniques can you use in order to evaluate the performance of clustering models?

7

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What preprocessing steps are typically involved in handling unstructured data for analysis?

8

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Explain the purpose of regularization techniques like L1 & L2 regularization in linear & logistic regression.

9

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What is the main difference between L1 and L2 regularization techniques?

10

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