Machine Learning Interview Questions

Taha U. - "In details: setting k=1 in KNN makes the model fit very closely to the training data, capturing a lot of the data's noise and leading to a model that may not generalize well to unseen data. This results in a high-variance scenario."See full answer

Abinash S. - "While running the testloop I am getting an error RuntimeError: runningmean should contain 28 elements not 38. I think it's the difference between the categorical features in train and test. `"See full answer

Ilnur I. - "I can try to summarize their discussion as I remembered. Linear regression is one of the method to predict target (Y) using features (X). Formula for linear regression is a linear function of features. The aim is to choose coefficients (Teta) of the prediction function in such a way that the difference between target and prediction is least in average. This difference between target and prediction is called loss function. The form of this loss function could be dependent from the particular real"See full answer

Ihuoma remita U. - "through the combination of online resources, hands on project and community engagement."See full answer

L B. - "For data distribution drift: DL Divergence or PSI (Population Stability Index) performance: two categories: 1st operational metrics: runtime. 2nd model performance: loss function, MAE (regression), business metrics: overall watch time, DAU, revenue lift etc Outlier: data distribution"See full answer

Frank A. - "1) create the experimental and control groups. 2) Then calculate the proportion (mean) of the true conversion rates for both groups using the convert column which counts True as 1 and False as 0. This is their conversion rates 3) calculate the statistic of the two groups by subtracting the proportion and standardizing. 4) get the p-value and compare with 0.05. 5) conclude the difference is statistically significant if the p-value is less than 0.05 otherwise no statistical difference"See full answer

Abinash S. - "I checked the unittest is giving a False assertion as you can see in the colab notebook below. F FAIL: testsimple (main_.Conv2dTest) Traceback (most recent call last): File "", line 19, in test_simple self.assertTrue(torch.equal(output, torch.tensor([[[[ 5., 1.], [ -2., -10.]]]]))) AssertionError: False is not true"See full answer

Deepti K. - "I gave multiple answers including polling the service every 10 sec to see customer. Or we can have the client side call which will send this data after 10 sec to us. We will store in dynamo DB and then send through pipelines to redshift DB for analytics."See full answer

Zain R. - "I've worked on projects not quite like this, but very similar, in the past - I'll borrow from that to answer this: The Broader Context this problem doesn't specify the type of data we're working with, or how it's being ingested to align with my personal background, I'll assume a picture that lends this problem well to being a computer vision (abbreviated "CV") related question: let's say we have a conveyor belt in a waste facility, which sequentially carries a stream of waste w"See full answer

Ayush B. - "focus was on tradeoffs of diff object detection algorithms, data collection and labelling, foundational models, followup: not working well in production, retraining/active learning"See full answer