# decision trees

Published articles for decision trees.

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## Decision Trees and Political Party Classification

DevFeed: [Decision Trees and Political Party Classification](<https://devfeed.tech/articles/decision-trees-and-political-party-classification-40289.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2012/10/08/decision-trees-and-political-party-classification/>)

Published: 2012-10-08T14:49:21Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [Python](<https://devfeed.tech/topics/python.md>), [data](<https://devfeed.tech/topics/data.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [decision-trees](<https://devfeed.tech/tags/decision-trees.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [politics](<https://devfeed.tech/tags/politics.md>), [python](<https://devfeed.tech/tags/python.md>), [voting-prediction](<https://devfeed.tech/tags/voting-prediction.md>)

### AI overview

This tutorial explains how decision trees address limitations of k-nearest-neighbors when data is categorical, incomplete, or noisy. It presents a Python implementation applied to predicting the political party affiliation of US congressional members from their votes.

### Source excerpt

Last time we investigated the k-nearest-neighbors algorithm and the underlying idea that one can learn a classification rule by copying the known classification of nearby data points. This required that we view our data as sitting inside a metric space; that is, we imposed a kind of geometric structure on our data. One glaring problem is that there may be no reasonable way to do this. While we mentioned scaling issues and provided a number of possible metrics in our primer, a more common problem is that the data simply isn't numeric.

## Machine Learning -- Introduction

DevFeed: [Machine Learning -- Introduction](<https://devfeed.tech/articles/machine-learning-introduction-40283.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2012/08/04/machine-learning-introduction/>)

Published: 2012-08-04T19:59:42Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [data](<https://devfeed.tech/topics/data.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [graph theory](<https://devfeed.tech/topics/graph-theory.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [data](<https://devfeed.tech/tags/data.md>), [decision-trees](<https://devfeed.tech/tags/decision-trees.md>), [graph-theory](<https://devfeed.tech/tags/graph-theory.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [support-vector-machines](<https://devfeed.tech/tags/support-vector-machines.md>)

### AI overview

This introduction presents machine learning as a broad toolbox that draws on optimization, statistical inference, linear algebra, graph theory, functional analysis, and topology. It outlines a recurring process of developing algorithms or mathematical models, testing them on datasets, and refining them with domain knowledge, while emphasizing mathematical foundations, implementation efficiency, and the structure of natural data.

### Source excerpt

A Series on Machine Learning These days an absolutely staggering amount of research and development work goes into the very coarsely defined field of "machine learning." Part of the reason why it's so coarsely defined is because it borrows techniques from so many different fields. Many problems in machine learning can be phrased in different but equivalent ways. While they are often purely optimization problems, such techniques can be expressed in terms of statistical inference, have biological interpretations, or have a distinctly geometric and topological flavor.