Just found that you can specify number of features when loading libsvm
source:
val df = spark.read.option("numFeatures", "100").format("libsvm")
Liang-Chi Hsieh wrote
> As the libsvm format can't specify number of features, and looks like
> NaiveBayes doesn't have such parameter, if your train
As the libsvm format can't specify number of features, and looks like
NaiveBayes doesn't have such parameter, if your training/testing data is
sparse, the number of features inferred from the data files can be
inconsistent.
We may need to fix this.
Before a fixing going into NaiveBayes, currentl
Hi Jinhong,
Based on the error message, your second collection of vectors has a
dimension of 804202, while the dimension of your training vectors
was 144109. So please make sure your test dataset are of the same dimension
as the training data.
>From the test dataset you posted, the vector dimens
Anyone help?
> 在 2017年3月13日，19:38，jinhong lu 写道：
>
> After train the mode, I got the result look like this:
>
>
> scala> predictionResult.show()
>
> +-++++--+
> |label|features| rawPre
After train the mode, I got the result look like this:
scala> predictionResult.show()
+-++++--+
|label|features| rawPrediction|
probability|prediction|
+-+
