Non-iid data

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover non-iid data?

Defines dependence and distribution differences, gives grouped-data examples, and identifies effects on bootstrapping, subsampling, and cross-validation.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Non-IID data consists of observations that are dependent, drawn from different distributions, or both.

Non-IID data contains observations that are dependent, drawn from different distributions, or both. Unlike repeated flips of the same coin, where outcomes are independent and identically distributed, non-IID observations often reflect groups, repeated measurements, or changing conditions. section 2.1.1

A practical pattern

Sales from multiple stores are non-IID when transactions from the same store are correlated and store dynamics differ. School surveys can show the same pattern because students in a class share characteristics and experiences. Repeated measures and time-series observations introduce dependence as well. section 2.1.1

A visible signal is a change in the sequence’s distribution: values may appear stable at first and then jump after some observations. Such structure means row order and sampling choices can carry information rather than being harmless bookkeeping. section 2.1.1

Account for non-IID structure when you:

Create cross-validation folds, because random sampling may separate related observations or groups in misleading ways. section 2.1.1 section 2.5
Use bootstrapping or subsampling, since dependence and distribution differences can affect their validity. section 2.5
Explore the data with value counts, descriptions, histograms, box plots, correlation matrices, or low-dimensional projections to reveal group structure and problems. section 2.5

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

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Related concepts

datakind of · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
time-series modeladdressed by · A time-series model represents or analyzes observations ordered over time to characterize temporal patterns or dependencies and support inference or prediction.
temporal featureaddressed by · A temporal feature is an input variable derived from dates, time intervals, or the temporal position or cycle of an observation.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.