In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study
Logistic regression is a technique that uses statistics to develop a prediction model on any occurre...
Overfitting is a common problem in the development of predictive models. It leads to an optimistic e...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
In this paper a new model validation procedure for a logistic regression model is presented. At firs...
Application of logistic regression modeling techniques without subsequent performance analysis regar...
Abstract: – Assessment of the quality of the logistic regression model is central to the conclusion...
Assessment of the quality of the logistic regression model is central to the conclusion. Application...
Logistic regression is a sophisticated statistical tool for data analysis in both control experiment...
Performance metrics of the prediction models using logistic regression and random forest methods wit...
OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression mode...
A tree procedure is proposed to check the adequacy of a fitted logistic regression model. The propos...
A tree procedure is proposed to check the adequacy of a fitted logistic regression model. The propos...
textabstractWe conducted an extensive set of empirical analyses to examine the effect of the number ...
A regressive logistic model for the analysis of data with dependent binary observations is construct...
Logistic regression is a technique that uses statistics to develop a prediction model on any occurre...
Logistic regression is a technique that uses statistics to develop a prediction model on any occurre...
Overfitting is a common problem in the development of predictive models. It leads to an optimistic e...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
In this paper a new model validation procedure for a logistic regression model is presented. At firs...
Application of logistic regression modeling techniques without subsequent performance analysis regar...
Abstract: – Assessment of the quality of the logistic regression model is central to the conclusion...
Assessment of the quality of the logistic regression model is central to the conclusion. Application...
Logistic regression is a sophisticated statistical tool for data analysis in both control experiment...
Performance metrics of the prediction models using logistic regression and random forest methods wit...
OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression mode...
A tree procedure is proposed to check the adequacy of a fitted logistic regression model. The propos...
A tree procedure is proposed to check the adequacy of a fitted logistic regression model. The propos...
textabstractWe conducted an extensive set of empirical analyses to examine the effect of the number ...
A regressive logistic model for the analysis of data with dependent binary observations is construct...
Logistic regression is a technique that uses statistics to develop a prediction model on any occurre...
Logistic regression is a technique that uses statistics to develop a prediction model on any occurre...
Overfitting is a common problem in the development of predictive models. It leads to an optimistic e...
When performing a regression or classification analysis, one needs to specify a statistical model. T...