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Machine Learning using Stata/Python

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Giovanni Cerulli

Computation · Submitted 3 Mar 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular Machine Learning (ML) methods both in regression and classification settings.

how it is stated · asserted 1.00 read the record →

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arXiv ID2103.03122 (version 1)
Pith ID44PD265Q, Pith's permanent record of this paper and its review
CitationGiovanni Cerulli. "Machine Learning using Stata/Python." arXiv:2103.03122 (2021). https://pith.science/paper/44PD265Q
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,951 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.05 · human participants 0.07 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.05 · mentions code or data availability 0.18.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular Machine Learning (ML) methods both in regression and classification settings. Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyper-parameters' optimal tuning via K-fold cross-validation using greed search. More specifically, they make use of the Python Scikit-learn API to carry out both cross-validation and outcome/label prediction.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular Machine Learning (ML) methods both in regression and classification settings. contribution 1.00 0.90 0.25 asserted 1.00
s1 Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyper-parameters' optimal tuning via K-fold cross-validation using greed search. method 0.94 0.75 0.11 asserted 1.00
s2 More specifically, they make use of the Python Scikit-learn API to carry out both cross-validation and outcome/label prediction. method 1.00 0.68 0.06 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.85.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • T body sentences
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  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular Machine Learning (ML) methods both in regression and classification settings. Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyper-parameters' optimal tuning via K-fold cross-validation using greed search. More specifically, they make use of the Python Scikit-learn API to carry out both cross-validation and outcome/label prediction.

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