How do you encoding categorical data with high cardinality?

How do you encoding categorical data with high cardinality?

Encoding of categorical variables with high cardinality

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}.

What do you do with high cardinality categorical variables?

In the supervised Machine Learning context, where class or target variables are available, high cardinality categorical attribute values can be can be converted to numerical values. The encoding algorithms are based on correlation of such categorical attributes to the target or class variables.

What are high cardinality categorical features?

A categorical feature is said to possess high cardinality when there are too many of these unique values. One-Hot Encoding becomes a big problem in such a case since we have a separate column for each unique value (indicating its presence or absence) in the categorical variable.

How do you encode high cardinality features?

How handle high cardinality

1. Label Encoder : Replace string values by integer classes [0, 1, 2, 3…]
2. Dummy Encoder : This method consist on creating n new variables of.
3. Aggregating Values : This method consist on aggregating values with low cardinality by creating a “Others” class.

Why high cardinality is bad?

Improper encoding of high-cardinality features can lead to poor model performance, and memory issues when trying to fit the model because encoding can result in extremely large matrices of values.

Why is high cardinality bad?

Why high cardinality is a problem?

To clear up one common point of confusion: high cardinality has only become such a big issue in the time series world because of the limitations of some popular time series databases. In reality, high cardinality data is actually a solved problem, if one chooses the right database.

Is high cardinality good or bad?

Having high cardinality data isn’t a bad thing, and knowing that our data is complex can help us find issues specifically tied to this. If you have performance or stability issues in your database, then it’s worth trying to lower the cardinality to fix those problems.

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.

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 encode columns with high cardinality in Python?

There you have many different encoders, which you can use to encode columns with high cardinality into a single column. Among them there are what are known as Bayesian encoders, which use information from the target variable to transform a given feature.

Why is mean encoding used in classification models?

While mean encoding has shown to increase the quality of a classification model, it suffers from overfitting. The fact that we are encoding the feature based on target classes may lead to data leakage, rendering the feature biased. To solve this, mean encoding is usually used with some type of Regularization.