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Is a smaller AIC better?
In plain words, AIC is a single number score that can be used to determine which of multiple models is most likely to be the best model for a given dataset. It estimates models relatively, meaning that AIC scores are only useful in comparison with other AIC scores for the same dataset. A lower AIC score is better.
Why is more training data better?
Increasing the training data always adds information and should improve the fit. The difficulty comes if you then evaluate the performance of the classifier only on the training data that was used for the fit.
Which is better AIC or BIC?
AIC is best for prediction as it is asymptotically equivalent to cross-validation. BIC is best for explanation as it is allows consistent estimation of the underlying data generating process.
How does the size of the dataset affect training?
Your training and test errors are affected by the size of the training. Take a look to this plot, usually known as a learning curve: In this example, we compute the training score and the test score (cross validation score) of a Naive Bayes model as we increase the number of examples in the training dataset.
How are AIC scores used in model selection?
In statistics, AIC is most often used for model selection. By calculating and comparing the AIC scores of several possible models, you can choose the one that is the best fit for the data. When testing a hypothesis, you might gather data on variables that you aren’t certain about, especially if you are exploring a new idea.
How does training and test size affect machine learning?
A larger training set decreases the score because it is more difficult for the learning algorithm to learn a model that correctly represents all the training data. However, as we increase the size of the training set, the test score also increases, due to an increase in the model’s ability to generalise.
How are probabilistic models selected with AIC and Bic?
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.