How do you handle categorical features with high cardinality?

How do you handle categorical features with high cardinality?

Dealing with categorical features with high cardinality: Target…

1. Label Encoding (scikit-learn): i.e. mapping integers to classes.
2. One Hot / Dummy Encoding (scikit-learn): i.e. expanding the categorical feature into lots of dummy columns taking values in {0,1}.

How do you handle cardinality?

Common solutions are : Label Encoder : Replace string values by integer classes [0, 1, 2, 3…]…II. Treat High cardinality

1. Dummy Encoder. The Dummy Encoder solution consists in creating n new dummy variables (columns) that contains 1 and 0 denoting the presence of the feature or not.
2. Aggregating values.
3. Target Encoding.

What’s the cardinality of sets?

The cardinality of a set is a measure of a set’s size, meaning the number of elements in the set. For instance, the set A = { 1 , 2 , 4 } A = \{1,2,4\} A={1,2,4} has a cardinality of 3 for the three elements that are in it.

What is cardinality violation?

Cardinality violations occur when a query that should return only a single row returns more than one row to an Embedded SQL™ application.

How to deal with categorical features with high cardinality?

One Hot / Dummy Encoding ( scikit-learn ): i.e. expanding the categorical feature into lots of dummy columns taking values in {0,1}. This is infeasible for categorical features having e.g. >10,000 unique values. I understand that models will struggle with sparse and large data.

How to encoding high cardinality categorical variables in spark?

With high cardinality categorical variables, popular encoding solutions like One Hot Encoding is not feasible. In this post we will go through a technique called Feature Hashing for encoding high cardinality categorical variables as implemented on Spark. We will showcase the solution with a use case from mobile advertisement.

Which is the Hot / dummy encoding for categorical variables?

Label Encoding ( scikit-learn ): i.e. mapping integers to classes. While it returns a nice single encoded feature column, it imposes a false sense of ordinal relationship (e.g. 135 > 72). One Hot / Dummy Encoding ( scikit-learn ): i.e. expanding the categorical feature into lots of dummy columns taking values in {0,1}.

How to encoding categorical variables with feature hashing?

The encoding based on feature hashing is implemented by the Spark job CategoricalFeatureHashingEncoding. The encoding vector size as estimated should be provided as a configuration parameter. Other important parameter are hash functions for indexing and choosing sign.