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Answer: Crossing latitude and longitude features, bucketizing at the minute level, and applying L1 regularization during optimization
L1 regularization is preferred when you need to emphasize more influential features by shrinking less important ones to zero. L2 regularization is more suitable when all features have a similar impact on the output. For more details, refer to: [Google's Machine Learning Crash Course on Regularization](https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization).
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When training a neural network to predict housing prices using a dataset that includes latitude and longitude, what is the most effective method to account for the influence of location on price?
A
Directly inputting latitude and longitude as vectors into the neural network
B
Generating a numeric column by crossing latitude and longitude features
C
Crossing latitude and longitude features, bucketizing at the minute level, and applying L1 regularization during optimization
D
Crossing latitude and longitude features, bucketizing at the minute level, and applying L2 regularization during optimization
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