paper-with-me

Papers

Machine Learning using Stata/Python

2021-03-03 · Giovanni Cerulli

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.

📄 PDF Abstract BibTeX arXiv:2103.03122

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine Learningregression

Similar Papers 제목 키워드 기반

Julia as a universal platform for statistical software development

2024-04-14 · David Roodman

The julia package integrates the Julia programming language into Stata. Users can transfer data between Stata and Julia, issue Julia commands to analyze and plot, and pass results back to Stata. Julia's econometric ecosy…

PySDTest: a Python/Stata Package for Stochastic Dominance Tests

2023-07-20 · Kyungho Lee, Yoon-Jae Whang

We introduce PySDTest, a Python/Stata package for statistical tests of stochastic dominance. PySDTest implements various testing procedures such as Barrett and Donald (2003), Linton et al. (2005), Linton et al. (2010), a…

pystacked: Stacking generalization and machine learning in Stata

2022-08-23 · Achim Ahrens, Christian B. Hansen, Mark E. Schaffer

pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python's scikit-learn. Stacking combines multiple supervised machine learners -- the "base" or "level-0" learners -…

Binary Classificationregression

ddml: Double/debiased machine learning in Stata

2023-01-23 · Achim Ahrens, Christian B. Hansen, Mark E. Schaffer, Thomas Wiemann

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects …

StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems

2024-05-20 · Pavlos S. Bouzinis, Panagiotis Radoglou-Grammatikis, Ioannis Makris, Thomas Lagkas 외

Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a th…

Federated LearningIntrusion DetectionPrivacy Preserving