AIC and BIC are both model selection criteria used to compare and rank different models. They help you choose the best model for your data by evaluating the trade-off between the fit of the model and its complexity.

1️⃣ AIC (Akaike Information Criterion): It is used to evaluate the quality of a model by penalizing the number of parameters in the model. The AIC score is calculated as the negative log-likelihood of the observed data, adjusted by the number of parameters in the model.

The model with the lowest AIC score is generally considered to be the best model for the given data. ⭐ This score is a good balance between the fit of the model and its complexity, making it a popular choice for model selection.

2️⃣ BIC (Bayesian Information Criterion): It's similar to AIC, but it penalizes the number of parameters in the model more heavily. BIC score is calculated as the negative log-likelihood of the observed data, adjusted by the number of parameters in the model and the sample size.

The BIC score is intended to balance the fit of the model to the data with the complexity of the model. ⭐ It's often used when the sample size is large, and you want to avoid overfitting.

So, when choosing between models, you can use either AIC or BIC to help you find the best one. Just remember, the best model depends on the problem you're trying to solve and the data you're working with.

@daansan_ml It‘s important to find the right model! Well explained, David.

@tinztwins Thanks!

@daansan_ml Important to choose the right model and these methods are very helpful 👍

@daansan_ml Great share David!

@daansan_ml Choosing the right model is very important. Great post 👏

@daansan_ml That's a good point. It's worth considering their implications.

@daansan_ml How to get the likelihood of the model of ARIMA?

@daansan_ml Hey there, David Andrés! I'm always up for exploring and finding the best models 😉 Let's dive into AIC and BIC together! 📊✨

@daansan_ml I just learnt something today.

@daansan_ml Love the visuals

@daansan_ml @CodeWithDF