How do you deal with features with high cardinality?

Reducing Cardinality by using a simple Aggregating function Leave instances belonging to a value with high frequency as they are and replace the other instances with a new category which we will call other. Keep adding the frequency of these sorted (descending) unique values until a threshold is reached.

What are the methods for transforming categorical variables?

Below are the methods to convert a categorical (string) input to numerical nature:

• Label Encoder: It is used to transform non-numerical labels to numerical labels (or nominal categorical variables).
• Convert numeric bins to number: Let’s say, bins of a continuous variable are available in the data set (shown below).

Which algorithm is best for categorical variables?

Logistic Regression is a classification algorithm so it is best applied to categorical data.

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}.

How do you reduce cardinality?

The easiest and the quickest step you can take to reduce cardinality is to change your query parameter setting. You can reduce the number of possible values in the Page dimension by filtering out dynamic session/customer ID variables in the query parameter settings.

Can you transform a categorical variable?

Variable transformation is a way to make the data work better in your model. – Categorical Variable Transformation: is turning a categorical variable to a numeric variable. Categorical variable transformation is mandatory for most of the machine learning models because they can handle only numeric values.

Do you need to transform categorical data?

This means that categorical data must be converted to a numerical form. If the categorical variable is an output variable, you may also want to convert predictions by the model back into a categorical form in order to present them or use them in some application.

How do you handle categorical variables in machine learning?

Machine learning models require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an Ordinal Encoding and a One-Hot Encoding.

How do you convert categorical variables to continuous variables?

The easiest way to convert categorical variables to continuous is by replacing raw categories with the average response value of the category. cutoff : minimum observations in a category. All the categories having observations less than the cutoff will be a different category.

How do you manage categorical data?

One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.

How do you deal with categorical variables in machine learning?

How to deal with high cardinality categorical features in OHE?

However, when having a high cardinality categorical feature with many unique values, OHE will give an extremely large sparse matrix, making it hard for application. The most frequently used method for dealing with high cardinality attributes is clustering.

When is it worth bucketing a featuer with low cardinality?

Also, for individual featuers with low cardinality, it’s often worth bucketing them. In the above example, you may end up replacement values for A and C, and then bucketing B and D into an “Other” category (similar to Triskelion’s trick with COUNT replacement).

What is an example of categorical data?

Examples include breeds of dogs, words, or postal codes. These features are known as categorical and each value is called a category. You can represent categorical values as strings or even numbers, but you won’t be able to compare these numbers or subtract them from each other.

Can you turn a categorical feature into a popularity feature?

Yes. You turn a categorical feature into a “popularity” feature (how popular is it in train set). Some categorical features may appear exactly the same number of times, say 3 times in train set. The model lossy learns that these cat vars do not appear often.

https://www.youtube.com/watch?v=vrWYw8d2830