One hot encoding is a process by which numeric variables are converted into a form that could be provided to neural networks to do a better job in prediction.
One hot encoding is a process by which numeric variables are converted into a categorical form that could be provided to neural networks to do a better job in prediction.
One hot encoding is a process by which only the hottest numeric variable is retained for use by the neural network.
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Question 3
What do you use the tf.feature_column.bucketized_column function for?
1 point
To compute the hash buckets needed to one-hot encode categorical values
To count the number of unique buckets the input values falls into
To discretize floating point values into a smaller number of categorical bins
None of the above
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Question 2
Which of these offers the best way to encode categorical data that is already indexed, i.e. has integers in [0-N]?