In some previous asked questions, I was told to not delete the outliers, because they contain valuable information.
After testing different regression, I came to the conclusion that until now, the
MARS regression delivers the "best responses".
I know that
MARS is very robust and there is no a priori knowledge about the data distribution needed.
But there are some question which I have about the parameters.
I'm using the
earth function implemented in
data set: file
So I've got 5 variables, price, livingArea, area, discrete, dummy and I'm trying to explain
price using the other ones.
as you can see, there are some outliers and a
log doesn't really solve the problem.
Due to the fact that
area can be
log won't be a good transformation idea.
what I do:
Because the answers from other questions suggested to use the raw data, I'm running now the regression through my data without doing any changes to it.
so my regression formula looks like this:
earth(price ~ ., data = data[,-1], weights = weights, penalty = -1)
penalty = -1 because I saw that doing this, the method defines more knots and also the results look better.
Also I tried to define the variables
factors and use them as follows in the regression:
- livingArea * discrete or livingArea : discrete and
- the same as at
- livingArea * discrete * dummy
I must say that I didn't expect, that a regression with this variables as factors, will return such "bad" results.
what I want:
I want to use the model in order to predict the value of new data.
livingArea area discrete dummy 1 87 0 7 0.5
The prediction of this observation should be
~ 330000, but with what I'm doing now, I ain't coming not even close to this value.
I think that, having more knots increases the precision of the result.
- I don't really understand the parameters
- I created my model with different values for the
pmethod, but the result was always the same. what's the point in choosing a method when the result will be the same?
- how could I determine if I have to set/change the values of different parameters like