Topic 1 Question 27
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?
Eliminate features that are highly correlated to the output labels.
Combine highly co-dependent features into one representative feature.
Instead of feeding in each feature individually, average their values in batches of 3.
Remove the features that have null values for more than 50% of the training records.
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Answer: B Description: Best Choice out of given options.
👍 29[Removed]2020/03/27Should be B
👍 16[Removed]2020/03/19Ans: B A: correlated to output means that feature can contribute a lot to the model. so not a good idea. C: you need to run with almost same number, but you will iterate twice, once for averaging and second time to feed the averaged value. D: removing features even if it 50% nulls is not good idea, unless you prove that it is not at all correlated to output. But this is nowhere so can remove.
👍 8anji0072021/10/12
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