How to make a logistic regression model better?
Answers from top 6 papers
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| Papers (6) | Insight |
|---|---|
45 Citations | The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model. |
30 Citations | In this study, we propose further modifications of the logistic regression model that do not require any additional variables that may be hard to measure. |
37 Citations | Logistic regression is useful for health-related research in which outcomes of interest are often categorical. |
25 Jun 2018 7 Citations | We show that the proposed framework yields better results compared to standard logistic regression model. |
| The resulting high overall prediction power demonstrates the robustness of the proposed logistic regression models. | |
| Experiments show that it is faster than the commonly used quasi Newton approach for logistic regression. |
Related Questions
How can logistic regression be used to improve public health outcomes?4 answersStep 1:
Logistic regression can be used to predict binary outcomes, such as road traffic fatalities, and identify risk factors for diseases like prostate cancer, thus contributing to public health interventions. It has been employed to model the likelihood of fatal accidents based on sex, and to identify significant risk factors for prostate cancer, including urban residency, smoking, age, and family history. Additionally, logistic regression has been widely used in the medical field and has yielded impressive results in predicting outcomes related to public health, such as hospital readmissions and disease risk factors.
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Step 3:
Logistic regression can be used to predict binary outcomes, such as road traffic fatalities, and identify risk factors for diseases like prostate cancer, thus contributing to public health interventions. It has been employed to model the likelihood of fatal accidents based on sex, and to identify significant risk factors for prostate cancer, including urban residency, smoking, age, and family history. Additionally, logistic regression has been widely used in the medical field and has yielded impressive results in predicting outcomes related to public health, such as hospital readmissions and disease risk factors.
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