What are the common loss functions used in machine learning?
This question assesses your familiarity with objective functions used to train models for both regression and classification tasks.
Why Interviewers Ask This
Loss functions guide the optimization process. Interviewers ask this to ensure you know which function to use for which problem type. They also want to see if you understand the properties of different losses, such as sensitivity to outliers.
How to Answer This Question
Categorize loss functions into Regression (MSE, MAE, Huber) and Classification (Cross-Entropy, Hinge, Log Loss). Briefly describe each: MSE squares errors, MAE uses absolute values, Huber is robust to outliers, and Cross-Entropy handles probabilities. Mention their specific applications, like SVMs using Hinge loss.
Key Points to Cover
- MSE is sensitive to outliers in regression.
- MAE is robust to outliers but less differentiable.
- Cross-Entropy is standard for classification.
- Huber Loss offers a robust alternative to MSE.
Sample Answer
Loss functions measure the error between predictions and actual targets to guide model training. For regression, Mean Squared Error (MSE) is common but sensitive to outliers, while Mean Absolute Error (MAE) is more robus…
Common Mistakes to Avoid
- Confusing regression and classification loss functions.
- Omitting Huber Loss or Hinge Loss.
- Not explaining the outlier sensitivity of MSE.
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