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分享一些自己总结的ML面试常见问题。自己遇到的问题基本都在里涵盖了。希望能帮到大家。. From 1point 3acres bbs
祝大家面试好运,都能拿到想要的offer!!
求米~急需大米看面经。如果觉得有用记得赏米哦~多谢!
1. How to check overfitting? How to deal with it?
2. Could you design a query embedding for Amazon teams?
.google и3. Ensemble algorithm? (Random forest; feature and data replacement; reduce variance
4. How to split a tree?
5. What metrics would you use in a classification problem? ..
6. How to deal with an imbalanced data set
7. What loss function will you use to measure multi-label problems
8. Let’s say now you want to identify a threshold for a classifier that predicts whether a customer will sign up to prime or not. What criteria could we use to find the threshold?.
9. In the model we developed, we have a billion positive samples and 200,000 negative samples. If you were to review our model before we put it on the website, what would you look for in the model to ensure this model is not bad?
10. Could you describe how you train this Context-awareness entity ranking model?. Waral dи,
11. 1. 解释regularization,为什么要regularization, l1和l2的区别
12. 2. 解释bagging和boosting, boosting要求每个iteration的training set都一样吗?(it depends). ----
13. 3. 说一些dimensions reduction的方法(我说了pca),ions of linear regression. ----
37. 7.How to deal with categorical features
38. 8. formular of logistic regression and the loss fun
39. 10. Explain how Kmeans is working
40. 11. If the linear regression dataset is duplicated, how would all the parameter change in your model?. Χ
41. 12. In linear regression, how will multicollinearity impact the cofficients and variance?
42. 13. If the correlated features should be excluded in random forest model?
43. 14. what is bias-variance trade-off.1point3acres
44. 15. how to deal with overfitting
45. 16. how to deal with imbalanced dataset
46. 17. How to deal with missing data
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