In general, R2 means the line that was fit is a very poor fit -- the
mean would give a smaller squared error. But it can also mean you are
applying R2 where it doesn't apply. Here, you're not performing a
linear regression; why are you using R2?
On Sun, Jul 12, 2015 at 4:22 PM, afarahat <ayman.fara...@yahoo.com> wrote:
> Hello;
> I am using the ALS recommendation MLLibb. To select the optimal rank, I have
> a number of users who used multiple items as my test. I then get the
> prediction on these users and compare it to the observed. I use
> the RegressionMetrics to estimate the R^2.
> I keep getting a negative value.
> r2 = -1.18966999676 explained var = -1.18955347415 count = 11620309
> Here is my Pyspark code :
>
> train1.cache()
> test1.cache()
>
> numIterations =10
> for i in range(10) :
> rank = int(40+i*10)
> als = ALS(rank=rank, maxIter=numIterations,implicitPrefs=False)
> model = als.fit(train1)
> predobs =
> model.transform(test1).select("prediction","rating").map(lambda p :
> (p.prediction,p.rating)).filter(lambda p: (math.isnan(p[0]) == False))
> metrics = RegressionMetrics(predobs)
> mycount = predobs.count()
> myr2 = metrics.r2
> myvar = metrics.explainedVariance
> print "hooo",rank, " r2 = ",myr2, "explained var = ", myvar, "count
> = ",mycount
>
>
>
>
> --
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