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Could you explain the tradeoff between bias and variance in machine learning and share an instance where you have observed this phenomenon in a model's performance?

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Answers

Anonymous

4Strong
bias is the average error of the model and variance is the variance of the model prediction. A model with high bias and low variance have predictions mapped into the same place in the space, a model with high variance and low bias has scattered predictions. The first one suggests that the model is too simple, it has high training error and testing error, the second one suggests overfitting when the model has low training error but high test error. I have seen this happen when a deep learning model starts overfitting, in that case I have used techniques to mitigate it like early stopping, regularization and dropout layer. 
  • Could you explain the tradeoff between bias and variance in machine learning and share an instance where you have observed this phenomenon in a model's performance?
  • What is the bias-variance tradeoff in machine learning, and how have you witnessed this tradeoff affecting the performance of a model?
  • Would you mind breaking down the interplay between bias and variance in machine learning and giving an example of how this tradeoff can emerge in a model's results?
  • In the realm of machine learning, what exactly is the bias-variance tradeoff, and can you describe an instance where you have seen this tradeoff manifest in a model's performance?
  • How would you characterize the balance between bias and variance in machine learning, and do you have an example of a case where this tradeoff has had an impact on the effectiveness of a model?
  • Can you walk me through the concept of bias and variance in machine learning, and offer a practical illustration of how this tradeoff can be apparent in a model's performance?
  • What is the meaning of bias-variance tradeoff in machine learning? Could you provide a real-life scenario where you have observed this tradeoff in a model's efficacy?
  • Explain how the bias-variance tradeoff functions in machine learning, and can you describe a specific scenario where this tradeoff has had a significant effect on a model's performance?
  • How does the bias-variance tradeoff in machine learning work, and have you ever seen this tradeoff become evident in the outcomes of a model? If so, could you share an example?
  • What is the relationship between bias and variance in machine learning, and can you give an example of how this tradeoff can be observed in a model's performance?
  • Describe the bias-variance tradeoff in the context of machine learning and provide a practical example of how this tradeoff can be observed in a model's performance?
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Interview question asked to Machine Learning Engineers interviewing at Duolingo, Venmo, Hewlett Packard and others: Could you explain the tradeoff between bias and variance in machine learning and share an instance where you have observed this phenomenon in a model's performance?.