51 Machine Learning Interview Questions With Answers

A Machine Learning interview calls for a rigorous interview process where the candidates are judged on various aspects such as technical and programming skills, knowledge of methods, and clarity of basic concepts. If you aspire to apply for machine learning jobs, it is crucial to know what kind of Machine Learning interview questions generally recruiters and hiring managers may ask.

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This is an attempt to help you crack the machine learning interviews at major product-based companies and start-ups. Usually, machine learning interviews at major companies require a thorough knowledge of data structures and algorithms. In the upcoming series of articles, we shall start from the basics of concepts and build upon these concepts to solve major interview questions. Machine learning interviews comprise many rounds, which begin with a screening test. This comprises solving questions either on the whiteboard or solving it on online platforms like HackerRank, LeetCode, etc.

Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka

7. What Is a False Positive and False Negative and How Are They Significant?

False positives are those cases that wrongly get classified as True but are False.Â

False negatives are those cases that wrongly get classified as False but are True.

In the term ‘False Positive,’ the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative.Â

So, looking at the confusion matrix, we get:

Similarly, in the term ‘False Negative,’ the word ‘Negative’ refers to the ‘No’ row of the predicted value in the confusion matrix. And the complete term indicates that the system has predicted it as negative, but the actual value is positive.

So, looking at the confusion matrix, we get:

158. What are the advantages of using a naive Bayes for classification?

  • Very simple, easy to implement and fast.
  • If the NB conditional independence assumption holds, then it will converge quicker than discriminative models like logistic regression.
  • Even if the NB assumption doesn’t hold, it works great in practice.
  • Need less training data.
  • Highly scalable. It scales linearly with the number of predictors and data points.
  • Can be used for both binary and mult-iclass classification problems.
  • Can make probabilistic predictions.
  • Handles continuous and discrete data.
  • Not sensitive to irrelevant features.
  • Machine Learning Interview Questions for Freshers

    Here, we have compiled a list of frequently asked top machine learning interview questions(ml interview questions) that you might face during an interview.

    140. What is the degree of freedom?

    Ans. It is the number of independent values or quantities which can be assigned to a statistical distribution. It is used in Hypothesis testing and chi-square test.

    36. How do we check the normality of a data set or a feature?

    Visually, we can check it using plots. There is a list of Normality checks, they are as follow:

  • Shapiro-Wilk W Test
  • Anderson-Darling Test
  • Martinez-Iglewicz Test
  • Kolmogorov-Smirnov Test
  • D’Agostino Skewness Test
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