How do you save a logistic regression model?
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37 Citations | Logistic regression is useful for health-related research in which outcomes of interest are often categorical. |
| The resulting fitted model may be used to estimate odds ratios in the same manner as is done with the usual logistic regression model. | |
49 Citations | Compared with the standard logistic regression model, the proposed model significantly improved the goodness-of-fit and predictive performance. |
| The proposed model can also produce more precise predicting accuracy than logistic regression model. | |
25 Jun 2018 7 Citations | We show that the proposed framework yields better results compared to standard logistic regression model. |
Related Questions
How does the logistic regression work?5 answersLogistic regression is a statistical technique used for binary outcomes, where the dependent variable is categorical. It models the probability of an event occurring based on one or more independent variables. Unlike linear regression, logistic regression deals with dichotomous qualitative response variables. The model estimates the probability of the event of interest happening by transforming the linear regression equation into a logistic function, ensuring the output is between 0 and 1. This transformation allows for the prediction of the likelihood of a binary outcome, such as yes/no or 1/0, making logistic regression suitable for scenarios like predicting disease contraction, readmission rates, customer behavior, or loan defaults. Logistic regression coefficients are obtained through methods like Wald's test and likelihood ratio test for hypothesis testing.
What are some common applications of logistic regression?5 answersLogistic regression is commonly used in various fields for risk assessment studies, such as credit risk assessment in financial institutions, estimation of risk factors for traffic accidents, identification of etiological factors for chronic diseases, and modeling a dichotomous response variable in biomedical research. It is also widely used in the financial service industry for binary classification tasks, such as building credit scorecards, estimating the probability of default or churn, and identifying the next best product in marketing. Logistic regression models are used when the outcome of interest is binary, and they produce odds ratios that have a useful interpretation. The applications of logistic regression can range from medical research to banking and marketing, making it a versatile and valuable tool in data analysis and machine learning.
How does logistic regression work?5 answersLogistic regression is a modeling technique used to investigate the independent effect of a variable on a binary outcome in medical research. It allows for the use of both continuous and categorical variables and provides the ability to adjust for multiple predictors, making it useful for analyzing observational data. Unlike linear regression, logistic regression does not assume a linear relationship between the dependent and independent variables. Instead, it assumes a relationship between the logit of the outcome and the predictor values. Logistic regression is commonly used in the medical field for modeling binary outcomes and has yielded impressive results. It is particularly useful when the outcome variable has two outcomes (binary logistic regression) or more than two categories (multinomial logistic regression). Logistic regression models are also used for modeling dichotomous response variables in biomedical research and can be used for making statistical inferences about the relationship between the response variable and the explanatory variables. The goal of logistic regression is to create an equation that can estimate the probability of an event of interest based on independent variables.
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