Top Machine Learning Interview Questions

We have included the most often asked questions by interviewers in this Machine Learning Interview Questions blog.

1. What would you say to a school-aged child about Machine Learning?

ANS: Let's say a friend invites you to a party where you meet people you've never met before. Because you don't know who they are, you'll mentally categorize them by gender, age group, clothing, and so on. Strangers represent unlabeled data in this instance, and the process of classifying unlabeled data pieces is nothing more than unsupervised learning. This becomes an unsupervised learning challenge because you didn't utilize any prior knowledge about people and classed them on the fly.

2. What exactly do you mean when you say "selection bias"?

ANS: A bias in the sampling portion of an experiment is caused by a statistical inaccuracy. Because of the inaccuracy, one sampling group is chosen more frequently than the other groups in the experiment. If the selection bias is not identified, it may lead to an incorrect conclusion.

3. Define Over-fitting?

ANS: Over-fitting occurs when a model analyses the training data to the point where it has a negative impact on the model's performance on new data.

4. What is the difference between collinearity and multicollinearity?

ANS: When two predictor variables (e.g., x1 and x2) in a multiple regression exhibit some association, this is called collinearity. When more than two predictor variables (e.g., x1, x2, and x3) are inter-correlated, this is known as multicollinearity.

5. What is Cluster Sampling, and how does it work?

ANS: It's a method of picking intact groups with similar qualities at random from a specific population. A cluster sample is a probability sample in which each sampling unit is a group of elements. Managers (samples) will represent elements, and companies will represent clusters if you're clustering the total number of managers in a group of organizations.

6. What is the relationship between NumPy and SciPy?

ANS: SciPy includes NumPy. NumPy defines arrays as well as certain fundamental arithmetic functions such as indexing, sorting, and reshaping. NumPy's functionality is used by SciPy to implement computations such as numerical integration, optimization, and machine learning.

7. How do you match real names to nicknames (Pete, Andy, Nick, Rob, etc.)?

ANS: There are numerous solutions to this problem. Assume you've been handed a data set comprising thousands of Twitter exchanges. You'll start by examining the language used in the tweets to learn about the relationship between two persons. This type of problem can be solved by employing Natural Language Processing techniques to implement Text Mining, in which each word in a sentence is broken down and co-relations between multiple words are discovered. NLP is widely utilized in customer service and sentiment analysis on social media platforms such as Twitter and Facebook.

8. With a simple example, explain false negative, false positive, true negative, and true positive. Consider a scenario of a fire emergency.

ANS: True Positive: If the smoke detector goes off in the event of a fire. Fire is a good thing, and the system's forecast is correct. If the alarm goes off but there is no fire, it is a false positive. The system projected that fire would be positive, which is incorrect, and therefore the forecast is false. If the alarm does not go off but there is a fire, this is a False Negative. The system anticipated that fire would be negative, which was incorrect because there was the fire. True Negative: If there was no fire and the alarm did not go off. This prophecy came true since the fire is negative.

9. What does the ROC curve indicate and what does it mean?

ANS: The Receiver Operating Characteristic curve (or ROC curve) is a depiction of the true positive rate (Sensitivity) against the false positive rate (Specificity) at various diagnostic cut-off points.

10. What is the difference between information gain and entropy?

ANS: Entropy is a measure of how jumbled up your data is. As you get closer to the leaf node, it gets smaller. The decrease in entropy after a dataset is split on an attribute is used to calculate the Information Gain. As you get closer to the leaf node, it continues to rise.

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