Post by account_disabled on Mar 11, 2024 0:04:19 GMT -5
Here, you are evaluating your candidate's ability to correctly visualize data, as well as their knowledge of popular tools, such as Plot.ly, Tableau, Python seaborn, and more. 39. What would you do if you discovered that data was missing or corrupted in a data set? Your candidate should state that they would look for missing or corrupt data and then replace it with another value or delete those columns or rows. 40. Define the F1 score. How is it used? The candidate should indicate that the F1 score is a way to measure the performance of a model and that they would use it in classification tests. 41. Explain the difference between type I and type II errors. This should be a very easy question for machine learning engineers, but it's wise to ask a few easy questions to ensure the candidate has mastered the basics. Type I error is a false positive. It claims that something has happened when it has not. Type II error is a false negative . He claims that nothing has happened when something has happened. 42. How does an ROC curve work? The candidate should explain that the ROC curve is a graph that represents two parameters, the true and false positive rates.
A key aspect to consider here is whether you understand that an ROC curve is typically used as a proxy for the trade-off between false positives, i.e. the probability of false alarms being triggered, versus true Bahamas Mobile Number List positives, i.e. the sensitivity which is the model. 43. Explain how your machine learning skills will help our company generate profits. This is a great question to see if your candidate has researched your company. A good machine learning engineer understands that his or her skills are only useful if they generate business results. Let's say you're hiring for Netflix. In that case, your candidate might say that by developing a more accurate recommendation model, users would be more satisfied with the shows they watch, leading to user retention and long-term profits. 44. Give me examples of your favorite machine learning models. This is another question to assess whether your candidate has more than just a "work" interest in machine learning.
A passionate machine learning engineer will give several examples of machine learning models that they like - and will be knowledgeable about how each one was implemented. 45. What do you think of our data processing? This type of question allows you to see if the candidate could be a valuable addition to your current team. A good candidate will demonstrate that they understand why the data process has been set up in a certain way. He will give you constructive and insightful feedback. 46. In machine learning, what are the three phases of building a model? It's a simple question, but it ensures that the candidate knows the basics. The three stages of model building in machine learning are: Model construction , in which the engineer chooses a suitable algorithm and trains it according to the criteria given to him. Model testing , the engineer uses test data to check the accuracy of the model. Model application , the engineer makes the necessary modifications after testing and starts using the model in real time. It is also a good sign if the candidate mentions that, once the model application phase is complete, they will need to check it from time to time to ensure that it is working correctly and is up to date.
A key aspect to consider here is whether you understand that an ROC curve is typically used as a proxy for the trade-off between false positives, i.e. the probability of false alarms being triggered, versus true Bahamas Mobile Number List positives, i.e. the sensitivity which is the model. 43. Explain how your machine learning skills will help our company generate profits. This is a great question to see if your candidate has researched your company. A good machine learning engineer understands that his or her skills are only useful if they generate business results. Let's say you're hiring for Netflix. In that case, your candidate might say that by developing a more accurate recommendation model, users would be more satisfied with the shows they watch, leading to user retention and long-term profits. 44. Give me examples of your favorite machine learning models. This is another question to assess whether your candidate has more than just a "work" interest in machine learning.
A passionate machine learning engineer will give several examples of machine learning models that they like - and will be knowledgeable about how each one was implemented. 45. What do you think of our data processing? This type of question allows you to see if the candidate could be a valuable addition to your current team. A good candidate will demonstrate that they understand why the data process has been set up in a certain way. He will give you constructive and insightful feedback. 46. In machine learning, what are the three phases of building a model? It's a simple question, but it ensures that the candidate knows the basics. The three stages of model building in machine learning are: Model construction , in which the engineer chooses a suitable algorithm and trains it according to the criteria given to him. Model testing , the engineer uses test data to check the accuracy of the model. Model application , the engineer makes the necessary modifications after testing and starts using the model in real time. It is also a good sign if the candidate mentions that, once the model application phase is complete, they will need to check it from time to time to ensure that it is working correctly and is up to date.