Who invented logistic regression

Which variant of logistic regression is recommended when you have a categorical dependent variable with more than two values? Skip to main content. Multiple Choice Quizzes. Answer: c. The logistic model is estimated by way of?

Ordinary least squares Maximum likelihood estimation Poisson distribution Negative binomial distribution. We demonstrate this from recent work on variables informing expectant mothers to opt for caesarean delivery or vaginal birth [49]. Tables are examples to illustrate the presentation of these four types of information.

Compared with babies with birth weight above 3. Table 2. Example of LR output: statistical tests of individual predictors. Table 3. Example of output from LR: overall model evaluation and goodness-of-fit statistics. Table 4. Example output from LR: model summary. Table 5. Example output from LR: a classification table.

That is, the relative probability of caesarean delivery decreases by It could also be seen from Table 2 that parity was estimated to be a significant predictor for the event. That is, compared with a pregnant woman with no parity and, all other variables held constant, expectant mothers with four or more parities are five times more as likely not to undergo caesarean delivery.

The relative probability of not undergoing caesarean delivery increases by From Table 3 two inferential statistical tests for overall model evaluation: the likelihood ratio and Wald tests, are shown. All two tests yield similar conclusions for the given data set. It could be noticed from the results of the likelihood ratio test and the Wald test presented in Table 3 that the logistic model with independent variables was more effective than the null model. Table 3 also presents the Hosmer-Lemeshow goodness-of-fit test.

This statistical test measures the correspondence of the actual and predicted expected values of the dependent variable caesarean delivery. A better model fit is characterized by insignificant differences between the actual and expected values. It tests the hypothesis H 0 , there is no difference between the predicted and actual values against H 1 , there is difference between the predicted and actual values.

At p-value of 0. A model summary of the logistic model is presented in Table 4. It could be observed that the model has a relatively larger pseudo R 2 of 0. This is an indication of a good model. Table 5 presents the degree to which predicted probabilities agree with actual outcomes in a classification table. The overall correct prediction, With the classification table, sensitivity, specificity, false positive and false negative can be measured.

Sensitivity measures the proportion of correctly classified events, whereas specificity measures the proportion of correctly classified nonevents.

The false positive measures the proportion of observations misclassified as events over all of those classified as events. The false negative therefore measures the proportion of observations misclassified as nonevents over all of those classified as nonevents.

This study explored the components LR model, a type of multivariable method used frequently for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on medical research.

Six text books on logistic regression and 37 research articles published between and which employed logistic regression as the main statistical tool were reviewed. Logistic regression concepts such as odds, odds ratio, logit transformation, logistic curve, assumption, selecting dependent and independent variables, fitting, reporting and interpreting were presented.

Upon perusing literature, considerable deficiencies were found in both the use and reporting of LR. Also, most studies did not report validation analysis, regression diagnostics or goodness-of-fit measures. We presented an example of how the LR should be applied. It is recommended that researchers be more thorough and pay greater attention to these guidelines concerning the use and reporting of LR models. In future, researchers could compare LR with other emerging classification algorithms to enable better or more rigorous evaluations of such data.

The idea was developed by EYB. Literature was reviewed by both authors. Both authors contributed to manuscript writing and approved the final manuscript.

Mathematical Geosciences, 43, Tinbergen Institute Working Paper. Journal of Clinical Epidemiology, 49, Political Analysis, 9, Journal of Advances in Mathematics and Computer Science, 26, Applied Logistic Regression, 1, Biometrics, 45, Atmospheric Research, 93, Expert Systems with Applications, 36, The r Evolution of Media, Publishing 2. American Journal of Political Science, 44, Annales Geophysicae, 23, Journal of Fluency Disorders, 38, Journal of College Student Development, 41, American Journal of Clinical Pathology, , Journal of Speech, Language, and Hearing Research, 32, The British Journal of Psychiatry, , Statistical Methods in Medical Research, 28, Journal of Clinical Epidemiology, 57, Journal of Clinical Epidemiology, 54, Annals of Internal Medicine, , Diabetologia, 52, In: Verma, M.

Methods in Molecular Biology, , Journal of the Royal College of Physicians of London, 28, British Medical Journal, , Statistical Methods in Medical Research, 9, Journal of Korean Academy of Nursing, 43, Critical Care, 9, Critical Care, 8, Statistica, 63, Statistics in Medicine, 15, CO; [ 46 ] Lee, J.

International Journal of Sustainable Transportation, 5, Open Journal of Applied Sciences, 9, Home Journals Article.

Ernest Yeboah Boateng , Daniel A. DOI: Abstract This study explored and reviewed the logistic regression LR model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on medical research.

Share and Cite:. Boateng, E. Journal of Data Analysis and Information Processing , 7 , Introduction Logistic regression LR analysis has become an increasingly employed statistical tool in medical research, especially over the last two decades [1] , although its origin can be dated back to the nineteenth century [2]. Materials and Methods 2.

The Logistic Regression Model The LR gives each predictor a coefficient which measures its independent contribution to variation in the dependent variable. The Logistic Curve The binary dependent variable has the values of 0 and 1 and the predicted value probability must be bounded to fall within the same range. Transforming a Probability into Odds and Logit Values The logistic transformation ensures that estimated values do not fall outside the range of 0 and 1.

Selecting the Dependent Variables In many cases, that outcome event is easily categorized into classes of having occurred, or not having occurred. Selecting Potential Predictors Another aspect to consider in the development of a LR study concerns the selection of which variables to analyse as potential predictors of the outcome.

Overall Model Evaluation 1 The likelihood ratio test The overall fit of a model shows how strong a relationship between all of the independent variables, taken together, and dependent variable is. Statistical Significance of Individual Regression Coefficients If the overall model works well, the next question is how important each of the independent variables is. Predictive Accuracy and Discrimination 2. Classification Table The classification table Table 1 is a method to evaluate the predictive accuracy of the logistic regression model [42].

Then a two-by-two table of data can be constructed with dichotomous Table 1. Conflicts of Interest The authors declare no conflicts of interest. I have the same question for random forests! And what is the definition of "machine learning"? Improve this question. Metariat Metariat 2, 4 4 gold badges 19 19 silver badges 41 41 bronze badges. Logistic Regression falls under ML because it is a classification algorithm. Machine Learning does not imply that the algorithm has to be adaptive although there are algorithms that learn from new observations.

Adapting is more an implementation choice, usually achieved by generative machine learning algorithms which model the joint probability. Really, all statistical procedures that involve fitting a model can be thought of machine learning.

Assuming the model fitting can be done by a computer, to some extent! This is why some statistician get frustrated with "big data", "machine learning", etc communities muddying the waters about what statistics is and isn't! Show 4 more comments. Active Oldest Votes. From WhatIs. From Wikipedia , Machine learning explores the construction and study of algorithms that can learn from and make predictions on data. Improve this answer. It has a large overlap, but there are types of learning, such as reinforcement learning, which can't really be considered to be a subset of statistics.

ML is a specialized field in statistics. Machine learning is the field that studies how machines can learn. I agree that most methods used in ML can be considered statistical methods, but the field is not inherently a subfield of statistics. For example, I do not think Markov decision processes are considered statistical methods. Once you estimate unknown parameters of a probability model e. Markov decision processes that is the textbook definition of a statistical procedure.

I think the main class of activities that can be called ML and not statistics are specific applications, like building a robot that plays chess. The underlying algorithms will undoubtedly involve probability and statistics, but the application isn't really "statistics". Kind of like how genomics research uses statistics heavily, but they are decidedly different fields.

Show 2 more comments. This may sound flippant, but I believe it to be the essence of the situation. Mark L. Stone Mark L. Stone However, for them to be appropriate as an answer on an SE site they need to have some kind of support. It would be cool if you could do that! But I have to agree with whuber that it doesn't really answer the question in its current form. I work in both software development and the maligned "Data Science".

I interview a lot of people. The rate of people interviewing for software development positions and data science positions who don't have the skills to do the job are about the same.

So what's special about the data science title? People are going to inflate their skills in all technical disciplines. I'm sure programming stack exchange has many of the same complaints.

Sure, names change, branding is important and machine learning is hot and hence has many self-proclaimed practitioners that don't know what they're doing. However, using that as an argument to downplay a field which has become established and highly relevant in both research and industry seems cheap to me. Stone I understand your situation and I completely agree that there are many incompetent insert hot term here 's out there.

However, in my opinion the fact such people find and keep! Any job that has a scent of cash has quacks, take medicine for instance. Show 25 more comments.

If I understood correctly, in a Machine Learning algorithm, the model has to learn from its experience That is not really how machine learning is usually defined.

Many regression methods are also classified as machine learning e. Marc Claesen Marc Claesen I always liked this definition of AI: en. Add a comment.

Frank Harrell Frank Harrell In that case, logistic regression doesn't "get it right the first time". I progressively learns. It has a standard loss, and its update is standard application of gradient descent. Logistic regression is in every machine learning text book that I've seen.

Also, the insistence that logistic regression only models probabilities, and is not, by itself, a classifier, is hair-splitting. By that logic, a neural network is not a classifier unless the output layer consists of binary neurons, but that would make backpropagation impossible. Digio Digio 2, 12 12 silver badges 17 17 bronze badges. I didn't say that ML is the nonparametric answer to statistics, I just find that ML methods being nonparametric comes as a side-effect.

Nonparametric statistics is an alternative option of the statistician when parametric statistics fails, but it's still the result of an expert's conscious choice.

I'm probably not being clear enough in communicating my view and for that I apologise. Have you heard of Empirical Likelihood - invented by a statistician, used by statisticians, and quite nonparametric, although it can also be used in a semi-parametric fashion. So I disagree with you, but I did not downvote you. Are you implying that nonparametric statistics has no need of machine learning something I never denied?

Or are you claiming that machine learning is in fact just another name for nonparametric statistics something I did deny?

Multivariable regression models, when used in conjunction with modern statistical tools, can be flexible and highly competitive with ML. Show 3 more comments. Matt Krause Matt Krause If you fit a model regression , that's statistical model fitting If you learn a model regression , that's machine learning So if you learn a logistic regression, that is a machine learning algorithm.

I can also learn a logistics model, what're you talking about? If you learn a logistics model, that is machine learning. The same thing can be called different things by different fields Statistics and Machine Learning. Antoine Antoine 5, 7 7 gold badges 29 29 silver badges 53 53 bronze badges. It makes no distributional assumptions whatsoever. It makes exactly the same kind of independence assumption made by ML.

ML requires much larger sample sizes than logistic regression.