Categorical data

As explained in Grokking Machine Learning

Data whose values belong to a finite set of named categories or states.

Is a type of data whose values come from a finite set of categories, or states. A label such as “cat” or “dog” is categorical; a weight such as 4.2 is numerical. In supervised learning, models that predict categories are called classification models. section 2.2 section 2.2

Categorical values can appear as labels or as features:

• Animal type (cat or dog) and email status (spam or ham) are categorical labels. section 2.2
• Gender and port of embarkation are categorical features in passenger data. section 13.3
• One-hot encoding converts categorical features to numbers; binning can turn numerical features into categories. section 13.3

Figure 13.1 One-hot encoding our data to turn it all into numbers for the machine learning model to read. On the left, we have columns with categorical features such as gender or port of embarkment. On the right, we have turned these categorical features into numerical features.

Compare categorical feature columns with their numerical representation after one-hot encoding. section 13.3

How Grokking Machine Learning covers it

How deep it goes

Defines finite categories and names one-hot encoding and binning as ways to convert between categorical and numerical representations.

Examples

Uses animal types, spam or ham labels, gender, and passenger embarkation ports as examples.

What you can do after

Apply one-hot encoding to categorical features or bin numerical features into categories before model training.

How it connects

Connects categorical data to supervised learning, classification, numerical data, feature engineering, one-hot encoding, and binning.

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  Causal AIA categorical variable is a variable whose values are labels from a finite set of categories rather than numerical measurements.

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Related concepts

• classifierproduced by · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• one-hot encodingused by · One-hot encoding represents each category as a binary vector or set of indicators with exactly one active component identifying that category.
• chi-square testused by · A chi-square test is a statistical hypothesis test that uses a chi-square-distributed statistic to evaluate hypotheses about categorical data, goodness of fit, or variance.
• discrete variableequivalent to · A discrete variable is a variable whose possible values belong to a finite or countable set of distinct alternatives rather than a continuous range.
• ordinal dataincludes · Ordinal data consists of categorical values with a meaningful order or rank, without necessarily having equal intervals between categories.
• nominal dataincludes · Nominal data consists of categorical values whose labels have no inherent or meaningful ordering.

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.