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What are common logistic regression mistakes and how can you avoid them?

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Logistic regression is a popular and powerful technique for modeling binary outcomes, such as whether a customer will buy a product, a patient will recover from a disease, or a student will pass an exam. However, it also comes with some potential pitfalls that can lead to misleading or inaccurate results. In this article, you will learn about some of the common logistic regression mistakes and how to avoid them.

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Ronny Daniel Gonzalez Roca

Scientific Advisor for Diagnostic Solutions
Eren Unlu

Co-Founder
Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance…

1 Mistake 1: Ignoring the assumptions

Logistic regression, like any other statistical method, has some assumptions that need to be checked before applying it to your data. These include linearity of the logit, independence of observations, absence of multicollinearity, and adequate sample size. If these assumptions are violated, your estimates may be biased, inconsistent, or inefficient. You can test these assumptions using various diagnostic tools, such as plots, tests, and indicators, and take corrective actions, such as transforming variables, removing outliers, or adding interaction terms.

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Eren Unlu

Co-Founder
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There is a reason it is called logistic regression : if you inverse the equation you would see that it is actually just a regular linear regression of an expression in natural logarithm (this is how it is solved by computers as well). So, assumptions of linear regression applies to it as well.

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
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neglecting logistic regression assumptions is akin to building on shaky ground. It's crucial to validate linearity, independence, multicollinearity, and sample size adequacy before trusting the results. Through practice, I've learned the importance of leveraging diagnostic tools for these checks, ensuring the model's reliability. Addressing violations with thoughtful transformations or adjustments, like handling outliers or introducing interaction terms, becomes paramount. This proactive approach not only enhances the model's accuracy but also fosters a deeper understanding of the data dynamics.

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2 Mistake 2: Using the wrong link function

Logistic regression is a special case of a generalized linear model (GLM), which allows you to choose different link functions to connect the linear predictor and the response variable. The most common link function for binary outcomes is the logit, which gives the log odds of the event. However, there are other link functions that may be more appropriate for some situations, such as the probit, which gives the standard normal cumulative distribution function, or the complementary log-log, which gives the log of the negative log of the survival function. You should choose the link function that best fits your data and research question, and compare the models using criteria such as the Akaike information criterion (AIC) or the Bayesian information criterion (BIC).

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3 Mistake 3: Interpreting the coefficients incorrectly

Logistic regression coefficients are not the same as linear regression coefficients. They do not represent the change in the response variable for a unit change in the predictor variable, but rather the change in the log odds of the event for a unit change in the predictor variable. To interpret them in a more meaningful way, you need to either exponentiate them to get the odds ratios, or use the inverse logit function to get the predicted probabilities. You should also be careful about interpreting the intercept, which is the log odds of the event when all the predictor variables are zero, which may not be a realistic or relevant scenario.

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Gülay Karao?lu

World Bank Finance Specialist/Teacher/Author-Researcher/Strategic Planner/Development Agency Specialist/Social Aid and Vocational T.P.P. Translator in the field of science and technicial books
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In one time I examined the subjects and the explanations. but if some time to explain the result and give the meaningful means you must also know the branch well which you examine. some times the seeings makes mistake of our interpretations.

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4 Mistake 4: Overfitting or underfitting the model

Logistic regression models can suffer from overfitting or underfitting, which means that they either capture too much or too little of the variability in the data. Overfitting occurs when you include too many predictor variables, especially irrelevant or highly correlated ones, or when you use too complex terms, such as higher-order polynomials or interactions. Underfitting occurs when you exclude important predictor variables, or when you use too simple terms, such as linear or additive ones. Both overfitting and underfitting can lead to poor predictive performance and generalization. You can avoid them by using techniques such as cross-validation, regularization, or model selection.

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5 Mistake 5: Neglecting the model evaluation

Logistic regression models need to be evaluated not only on their fit to the data, but also on their accuracy, precision, recall, specificity, and other metrics that reflect their performance on the outcome of interest. You should also assess the model's sensitivity to changes in the predictor variables, the cutoff values, or the data set. You can use methods such as confusion matrices, ROC curves, or calibration plots to evaluate your model and compare it with other models or benchmarks. You should also report the confidence intervals and p-values of your estimates, and discuss the limitations and implications of your findings.

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Ronny Daniel Gonzalez Roca

Scientific Advisor for Diagnostic Solutions
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Las curvas ROC son un buen estadístico a la hora de comparar modelos de regresión logística, tomando en cuenta la sensibilidad y especificidad de cada modelo se puede conocer cuál de ellos abarca una mayor área bajo la curva y por tanto mayor capacidad predictora.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are common logistic regression mistakes and how can you avoid them?

1

2

3

4

5

6

1 Mistake 1: Ignoring the assumptions

2 Mistake 2: Using the wrong link function

3 Mistake 3: Interpreting the coefficients incorrectly

4 Mistake 4: Overfitting or underfitting the model

5 Mistake 5: Neglecting the model evaluation

6 Here’s what else to consider

Statistics

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What are common logistic regression mistakes and how can you avoid them?

1

2

3

4

5

6

1 Mistake 1: Ignoring the assumptions

2 Mistake 2: Using the wrong link function

3 Mistake 3: Interpreting the coefficients incorrectly

4 Mistake 4: Overfitting or underfitting the model

5 Mistake 5: Neglecting the model evaluation

6 Here’s what else to consider

Statistics

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