Topic 1 Question 58
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?
Use feature construction to combine the strongest features.
Use the representation transformation (normalization) technique.
Improve the data cleaning step by removing features with missing values.
Change the partitioning step to reduce the dimension of the test set and have a larger training set.
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Vote for B. We could impute instead of remove the column to avoid loss of information
👍 25kurasaki2021/07/10I also think it is B: "The presence of feature value X in the formula will affect the step size of the gradient descent. The difference in ranges of features will cause different step sizes for each feature. To ensure that the gradient descent moves smoothly towards the minima and that the steps for gradient descent are updated at the same rate for all the features, we scale the data before feeding it to the model."
👍 7pddddd2021/09/28B. The problem does not mention anything about missing values. It needs to normalize the features with different ranges.
👍 4MK_Ahsan2022/01/08
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