CODEWITHSUNDEEP
statisticsregressionevaluationgoodness-of-fit
qubit
qubit

Reputation: 21

multivariate regression model (multiple input and multiple output) evaluation

I've been using R-squared (coefficient of determination) and mean-absolute-percentage-error to see the difference between true output value (scalar) and the predicted output value (also scalar) that come out of a regression model.

Now, I want to see how the regressed output (vector) is close to my true output (vector) in an intuitive way. MSE is used for the regression model's training, but it is hard to tell whether your model is doing OK or not. For example, if the true output value itself is very small (close to zero) and if your predicted output is twice times bigger than the true output, the MSE will be very small even though the prediction is twice as larger than the true output.

I've been searching a while, and I found terms like "wilk's lambda test", ANOVA, MANOVA, p-value, adjusted-R-squared. But I have not figured out what is the one I can and should use.

Upvotes: 1

Views: 475

Answers (1)

qubit
qubit

Reputation: 21

I just decided to use MAPE by using the Euclidean distance between vectors instead of the absolute value of the difference between scalars (predicted, true value).

Upvotes: 0

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