#x27;ve even tried x, y+(sample noise from normal with mean 0 and stddev 1)
>> still comes up with the same thing.
>>
>> Is this not supposed to work for x and y or 2 dimensional plots? Is there
>> something I'm missing or wrong in the code above? Or is there a limitation
ean 0 and stddev 1)
> still comes up with the same thing.
>
> Is this not supposed to work for x and y or 2 dimensional plots? Is there
> something I'm missing or wrong in the code above? Or is there a limitation
> in the method?
>
> Thanks for any advice.
>
>
Sent from my iPhone
Begin forwarded message:
> From: Robin East
> Date: 16 January 2015 11:35:23 GMT
> To: Joseph Bradley
> Cc: Yana Kadiyska , Devl Devel
>
> Subject: Re: LinearRegressionWithSGD accuracy
>
> Yes with scaled data intercept would be 5000 but
.label, prediction)
>> >}
>> >val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p),
>> 2)}.mean()
>> >println("training Mean Squared Error = " + MSE)
>> &
.pow((v - p),
> 2)}.mean()
> >println("training Mean Squared Error = " + MSE)
> >
> > Both scaled and unscaled attempts give:
> >
> > training Mean Squared Error = NaN
> >
> > I've even tried x, y+(sample noise from normal with mean 0 an
n 0 and stddev 1)
> still comes up with the same thing.
>
> Is this not supposed to work for x and y or 2 dimensional plots? Is there
> something I'm missing or wrong in the code above? Or is there a limitation
> in the method?
>
> T
with the same thing.
>
> Is this not supposed to work for x and y or 2 dimensional plots? Is there
> something I'm missing or wrong in the code above? Or is there a limitation
> in the method?
>
> Thanks for any advice.
>
>
>
> --
&g
pposed to work for x and y or 2 dimensional plots? Is there
something I'm missing or wrong in the code above? Or is there a limitation
in the method?
Thanks for any advice.
--
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