Description Usage Arguments Details Value Author(s) Examples
AIC stands for Akaike’s Information Criterion. It estimates the quality of a model, relative to each of other models. The lower AIC score is, the better the model is. Therefore, a model with lowest AIC - in comparison to others, is chosen.
1 | aic(y, y_pred, p)
|
y |
True target variable(s) - array-like of shape = (n_samples) or (n_samples, n_outputs) |
y_pred |
Fitted target variable(s) obtained from your regression model - array-like of shape = (n_samples) or (n_samples, n_outputs) |
p |
Number of predictive variable(s) used in the model - int |
AIC = n*log(residual sum of squares/n) + 2K where: - n: number of observations - K: number of parameters (including intercept)
double
Ha Dinh
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