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Joseph Berkson developed logistic regression as a general statistical model in 1944. Today, logistic regression is one of the main pillars of machine learning. From predicting Trauma and Injury Severity Score (TRISS) to sentiment analysis of movie reviews, logistic regression has umpteen applications in ML.

In a recent tweet, Bojan Tunguz, senior software engineer at NVIDIA, compared logistic regression to COBOL, a high-level programming language used for business applications.

It would be great if we could replace all of the logistic regressions with more advanced algos, but realistically we will never completely get rid of them, said Bojan.

COBOL first gained currency in 1970 when it became the de-facto programming language for business applications in mainframe computers around the world.

COBOLs relevance is chalked up to its simplicity, ease of use and portability.

Logistic regression is a simple classification algorithm used to model the probability of a discrete outcome from a given set of input variables. LR is used in supervised learning for binary classification problems.

Pierre Franois Verhulst published the first logistic function in 1838. Logistic regression was used in the biological sciences in the early twentieth century.

Source: dataaspirant.com

After Verhulsts initial discovery of logistic function, the most notable discoveries were the probit model, developed by Chester Ittner Bliss in 1934 and maximum likelihood estimation by Ronald Fisher in 1935.

In 1943, Wilson and Worcester used the logistic model in bioassay which was the first known application of its kind. In 1973 Daniel McFadden connected the multinomial logit to the theory of discrete choice, specifically Luces choice axiom. This gave a theoretical foundation for logistic regression, and earned McFadden a Nobel prize in 2000.

According to the global survey by Micro Focus, COBOL is viewed as strategic by 92 % of respondents.

Key findings of the Micro Focus COBOL Surveys include:

The importance of different AI/ML topics in organisations worldwide (2019). Source: Statista

Bojan Tunguzs tweet garnered both for and against responses.

While many said a simple solution that works should not be messed with, the opposite camp said complex algorithms like XGBoost provide better results.

Andreu Mora, an ML and Data science expert at Adyen payments, said: If a simple algorithm gets you a good performance it might not be a wise move to increase operational work by 500% for a 5% performance uplift.

To this, Bojan replied: Depends on the use case. If a 5% improvement in performance can save you $5B, then you totally should consider it.

Amr Malik, a research fellow at Fast.ai, said: For this scenario to be true, youd need to be supporting a $100 Billion dollar business operation with LR based models. Thatd be a gutsy bet on a really big farm.

We have picked the best responses from the tweet thread:

On LinkedIn, Damien Benveniste, an ML tech lead at Meta AI, said he never uses algorithms like logistic regression, Naive Bayes, SVM, LDA, KNN, Feed Forward Neural Network, etc. and relies only on XGBoost.

Read more here:
Is logistic regression the COBOL of machine learning? - Analytics India Magazine