# What is a good AIC for model fit?

The AIC function is 2K – 2(log-likelihood). Lower AIC values indicate a better-fit model, and a model with a delta-AIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to.

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## What is a good AIC for model fit?

The AIC function is 2K – 2(log-likelihood). Lower AIC values indicate a better-fit model, and a model with a delta-AIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to.

**What is AIC and BIC in model selection?**

AIC vs BIC. AIC and BIC are widely used in model selection criteria. AIC means Akaike’s Information Criteria and BIC means Bayesian Information Criteria. Though these two terms address model selection, they are not the same. One can come across may difference between the two approaches of model selection.

**What is AIC and BIC in statistics?**

The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) provide measures of model performance that account for model complexity. AIC and BIC combine a term reflecting how well the model fits the data with a term that penalizes the model in proportion to its number of parameters.

### Should I use BIC or AIC?

A point made by several researchers is that AIC and BIC are appropriate for different tasks. In particular, BIC is argued to be appropriate for selecting the “true model” (i.e. the process that generated the data) from the set of candidate models, whereas AIC is not appropriate.

**Is lower or higher BIC better?**

As complexity of the model increases, bic value increases and as likelihood increases, bic decreases. So, lower is better. This definition is same as the formula on related the wikipedia page.

**What AIC is too high?**

6.5% or above. A normal A1C level is below 5.7%, a level of 5.7% to 6.4% indicates prediabetes, and a level of 6.5% or more indicates diabetes. Within the 5.7% to 6.4% prediabetes range, the higher your A1C, the greater your risk is for developing type 2 diabetes.

## Why choose a model that minimizes AIC?

In AIC, we try to minimize the (proxy of) KL divergence between the model and the ground truth function. AIC is the calculation for the estimate of the proxy function. Thus minimizing the AIC is akin to minimizing the KL divergence from the ground truth — hence minimizing the out of sample error.

**Is a smaller BIC better?**

**Is a lower BIC better?**

### How do I choose a good BIC?

If the absolute difference δ is greater 10, the smaller BIC value is considerable better. If the absolute difference δ is greater 5, the smaller BIC value is likely to be better. If the absolute difference δ is smaller 2, the smaller BIC does not indicate to be better than the other.

**What is a good value for BIC?**

If it’s between 6 and 10, the evidence for the best model and against the weaker model is strong. A Δ BIC of greater than ten means the evidence favoring our best model vs the alternate is very strong indeed.

**Is a high BIC good?**

https://www.youtube.com/watch?v=-BR4WElPIXg