Discretization

As explained in Grokking Machine Learning

Binning is a technique that partitions numerical values into intervals or buckets so they can be represented as categorical groups.

bins = [0, 10, 20, 30, 40, 50, 60, 70, 80]
categorized_age = pandas.cut(preprocessed_data['Age'], bins)
preprocessed_data['Categorized_age'] = categorized_age
preprocessed_data = preprocessed_data.drop(["Age"], axis=1)

How Grokking Machine Learning covers it

How deep it goes

Defines binning as splitting values into buckets and demonstrates Pandas interval creation, without evaluating bin choices.

Examples

Uses Titanic passenger ages and decade-wide intervals from 0 to 80 as the concrete example.

What it assumes

Defines numerical and categorical features before applying bins to an age column.

What you can do after

Apply it via Pandas `cut` to replace passenger ages with decade-based categorical intervals.

How it connects

Connects feature engineering with numerical features, categorical features, and machine learning models.

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Also explained in

• 

  liveBookmanning.com
  Financial AI in PracticeBinning is the technique of grouping continuous or categorical values into discrete intervals or categories.
• 

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  Outlier Detection in PythonBinning is the process of assigning continuous or otherwise detailed values to discrete intervals or categories.
• 

  liveBookmanning.com
  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA technique that groups numeric values into discrete ordered bins and assigns each value to a bin.
• 

  liveBookmanning.com
  Grokking StatisticsBinning is the process of grouping continuous or discrete observations into intervals and summarizing each interval.

Compare how 5 books explain discretization →

Related concepts

• equal-width binningincludes · Equal-width binning is a discretization technique that partitions a numerical range into intervals of identical width.
• preprocessingkind of · Preprocessing is the preparation or transformation of data, content, or other inputs into a form suitable for a subsequent computational operation.
• continuous datauses · Continuous data consists of numerical measurements that can take arbitrarily fine-grained values across a range or interval.
• Chronosused by · Chronos is a foundation model and framework for probabilistic time-series forecasting that adapts language-model methods to temporal data.

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.