How to improve a logistic regression model?
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| The proposed model can also produce more precise predicting accuracy than logistic regression model. | |
01 Sep 1989 21 Citations | Estimation and testing of the logistic model indicated good model performance. |
18 Citations | STATISTICAL ANALYSIS Logistic regression. |
| The resulting high overall prediction power demonstrates the robustness of the proposed logistic regression models. |
Related Questions
What are the main limitations of logistic regressions?5 answersThe main limitations of logistic regressions include the risk of overfitting due to an overparameterized model, leading to implausibly large odds ratios and wide confidence intervals. Additionally, logistic regression may perform poorly when trained on noisy data, irrelevant features, or imbalanced class distributions, common in visual recognition tasks, unless addressed with regularization techniques or feature selection methods. Furthermore, logistic regression faces challenges in comparing coefficients across models due to changes in explanatory variables and the issue of multiplicity when conducting a large number of tests, which can result in spurious findings of significance if not properly managed. These limitations highlight the importance of careful model building, feature selection, and statistical adjustments when utilizing logistic regression in various applications.
How can logistic regression be improved to better identify fake news?5 answersLogistic regression can be improved to better identify fake news by using different approaches and techniques. One approach is to incorporate natural language processing (NLP) techniques, such as term frequency-inverse document frequency (TF-IDF) matrix, to analyze the words and their frequency in the dataset. Another improvement is to consider the specific characteristics of fake news articles, such as the absence of quotation marks, apostrophes, and dashes, and remove these characters from the dataset. Additionally, the use of machine learning algorithms like the Passive Aggression Classifier and Support Vector Machine (SVM) can enhance the accuracy of logistic regression in detecting fake news. These improvements can help logistic regression models achieve higher accuracy in identifying fake news in social media.
What are the limitations of the logistic regression models used in the study?3 answersLogistic regression models used in the studies have several limitations. One limitation is the bias of the odds ratio (OR) estimated using the maximum likelihood (ML) method, known as sparse data bias, which can lead to impossibly large values due to data sparsity. Another limitation is the potential for inflated estimates of the relative risk (RR) when the binary outcome has a high or moderate incidence in the population. Additionally, the additive structure of logistic regression assumes compensatory relationships between predictors, which may not accurately reflect the reality of ecological systems.
Why we should use logistic regression model?2 answersLogistic regression models are useful when the outcome of interest is binary, as they can model the binary outcome using the underlying logit function. They produce odds ratios that have a meaningful interpretation and can be used to understand the relationship between predictors and the binary outcome. Tips, tricks, and concepts used in fitting logistic regression models are similar to those used in linear regression models. It is preferred to build logistic regression models based on knowledge rather than automatic methods. Overparameterized models should be avoided, as they can result in implausibly large odds ratios and uninterpretable confidence intervals. Logistic regression models can be fit using standard statistical software. Logistic regression can be used as an alternative to traditional statistical hypothesis testing paradigms that require computing p-values. It allows for the localization of regions of brain network differences without the need for preselected feature vectors. Logistic regression is a powerful methodology for modeling a binary response variable and can be applied in various fields, including medical research, business analytics, and ecology. Logistic regression models are particularly useful when analyzing small, skewed, or sparse datasets, as traditional methods may not be reliable in these cases. Exact logistic regression models provide a method for testing and estimation in binary and nominal logistic models, especially in situations with small-sample or sparse data. Logistic regression models have unique properties when interpreting the dependent variable as a probability, allowing for the capture of information and certainty about the event of interest.
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