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Data Science #15 - The First Decision Tree Algorithm (1963)
In the 15th episode we went over the paper "Problems in the Analysis of Survey Data, and a Proposal" by James N. Morgan and John A. Sonquist from 1964.
It highlights seven key issues in analyzing complex survey data, such as high dimensionality, categorical variables, measurement errors, sample variability, intercorrelations, interaction effects, and causal chains.
These challenges complicate efforts to draw meaningful conclusions about relationships between factors like income, education, and occupation.
To address these problems, the authors propose a method that sequentially splits data by identifying features that reduce unexplained variance, much like modern decision trees.
The method focuses on maximizing explained variance (SSE), capturing interaction effects, and accounting for sample variability.
It handles both categorical and continuous variables while respecting logical causal priorities.
This paper has had a significant influence on modern data science and AI, laying the groundwork for decision trees, CART, random forests, and boosting algorithms. Its method of splitting data to reduce error, handle interactions, and respect feature hierarchies is foundational in many machine learning models used today.
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By Mike E
3.8
55 ratings
In the 15th episode we went over the paper "Problems in the Analysis of Survey Data, and a Proposal" by James N. Morgan and John A. Sonquist from 1964.
It highlights seven key issues in analyzing complex survey data, such as high dimensionality, categorical variables, measurement errors, sample variability, intercorrelations, interaction effects, and causal chains.
These challenges complicate efforts to draw meaningful conclusions about relationships between factors like income, education, and occupation.
To address these problems, the authors propose a method that sequentially splits data by identifying features that reduce unexplained variance, much like modern decision trees.
The method focuses on maximizing explained variance (SSE), capturing interaction effects, and accounting for sample variability.
It handles both categorical and continuous variables while respecting logical causal priorities.
This paper has had a significant influence on modern data science and AI, laying the groundwork for decision trees, CART, random forests, and boosting algorithms. Its method of splitting data to reduce error, handle interactions, and respect feature hierarchies is foundational in many machine learning models used today.
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