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Can you use AIC for logistic regression?
The AIC statistic is defined for logistic regression as follows (taken from “The Elements of Statistical Learning“): AIC = -2/N * LL + 2 * k/N.
Which criteria do you suggest to select the best logistic regression model?
1) The model (which you have selected) has all its variables significant or not basis the p value. 2) AIC/ BIC criterion to select the better model.
Which of the methods to use to get the best fit the data in logistic regression?
Just as ordinary least square regression is the method used to estimate coefficients for the best fit line in linear regression, logistic regression uses maximum likelihood estimation (MLE) to obtain the model coefficients that relate predictors to the target.
What does AIC stand for in logistic regression?
AIC is an estimate of the information lost when a given model is used to represent the process that generates the data. L be the maximum value of the likelihood function for the model. k is the number of independent variables.
How to calculate AIC for probabilistic model selection?
AIC = -2/N * LL + 2 * k/N Where N is the number of examples in the training dataset, LL is the log-likelihood of the model on the training dataset, and k is the number of parameters in the model. The score, as defined above, is minimized, e.g. the model with the lowest AIC is selected.
How to calculate Bic statistic for logistic regression?
The BIC statistic is calculated for logistic regression as follows (taken from “ The Elements of Statistical Learning “): Where log () has the base-e called the natural logarithm, LL is the log-likelihood of the model, N is the number of examples in the training dataset, and k is the number of parameters in the model.
How does the derivation of Bic relate to AIC?
Importantly, the derivation of BIC under the Bayesian probability framework means that if a selection of candidate models includes a true model for the dataset, then the probability that BIC will select the true model increases with the size of the training dataset. This cannot be said for the AIC score.