Interview Questions: Bias-Variance Tradeoff

How to Prepare for Bias-Variance Tradeoff Interview Questions

In this post, we’ll see a new way of studying the bias-variance tradeoff and how to prepare for data science and machine learning interview questions. Let’s start with a story from one of Korbit’s graduates and her successful interview experience.

What’s the Bias-Variance Tradeoff

The bias-variance tradeoff is a constant balancing act between adjusting the bias and the variance of an algorithm. In an ideal world, we could decrease both the bias and the variance, but in the real world, unfortunately, we need a tradeoff. For example, an improvement in bias would cause a higher variance and vice versa. It’s important therefore to understand the implications of this tradeoff in order to optimize machine learning algorithms. It is very common to have questions on this concept in data science and machine learning interviews.

Meet Taliana

Taliana, a masters student in cognitive neuroscience studying in Berlin completed the free course #LearnAIwithAnAI right before taking an interview for a data science internship. During the interview, she was asked to explain the concept of statistical bias (bias-variance tradeoff). She managed to explain the concept with confidence, in her own words, just like she had practiced on the Korbit platform. Here’s how Taliana learned about statistical bias with Korbi the AI tutor.

Studying Bias-Variance Tradeoff on Korbit

On Korbit, you watch bite-sized videos for each machine learning and data science concept and solve problems with your personal AI Tutor, Korbi. If you get stuck on a concept, Korbi helps you out. She’ll take a look in her activity toolbox and select the best activity based on your learning preference. Let’s see how Korbi can help us learn the bias-variance tradeoff.

Step 1: Watch short videos

Audrey Durand, an Assistant Professor in the Department of Computer Science/Software Engineering/Electrical and Computer Engineering at Université Laval (also affiliated with Mila) explains the main concepts of the bias-variance tradeoff in a short video. Audrey’s lectures on Korbit give an introduction to machine leaning fundamentals in the context of health-related applications.

Step 2: Answer questions

After watching the short video. Korbi, the AI tutor, will ask you questions on the concepts to check your understanding. You can type your answers in your own words and Korbi will give you feedback. Let’s suppose you’re not quite sure what the answer is and you type “I don’t know.” in the chatbox.

Step 3: Korbi to the rescue

Personalized Hints

Korbi now determines the best way to help you answer this question is to give you a hint about the definition. Notice that she doesn’t give you the answer right away, but tries to help you figure out the answer on your own.

In some cases, Korbi creates diagrams outlining the concepts in a hierarchy of elements making it easier to visualize how the concepts are connected. These are two examples out of more than a dozen pedagogical interventions Korbi uses to help users learn difficult concepts.

Concept Trees

Step 4: Try again

After getting some help, you can try answering the question again. Sometimes, you might need more help from Korbi before getting it right. One of Korbi’s qualities is patience and she’ll never get frustrated with your questions. She’ll be more than happy to help you learn the concept by providing different types of feedback and help.

Step 5: Learn new concepts!

Once you correctly answer a question, Korbi will ask you more questions while gradually increasing the difficulty and help you get to the next level. Machine learning and data science can be difficult to grasp, but with Korbi’s help you don’t need to face them alone.

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