- What is one-hot encoding?
- One hot encoding is a process by which categorical 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 only the hottest numeric variable is retained for use by the neural network.
- 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 numeric variables are converted into a form that could be provided to neural networks to do a better job in prediction.
- Which of these offers the best way to encode categorical data that is already indexed, i.e. has integers in [0-N]?
tf.feature_column.categorical_column_with_identity
tf.feature_column.categorical_column_with_vocabulary_list
tf.feature_column.categorical_column_with_hash_bucket
- What do you use the tf.feature_column.bucketized_column function for?
- To discretize floating point values into a smaller number of categorical bins
- To count the number of unique buckets the input values falls into
- To compute the hash buckets needed to one-hot encode categorical values