> though. E.g
>
> "lhs":0,"op":"<=","rhs":-35.0
> On Aug 28, 2015 12:03 AM, "Manish Amde" > wrote:
>
>> Hi James,
>>
>> It's a good idea. A JSON format is more convenient for visualization
>>
Hi James,
It's a good idea. A JSON format is more convenient for visualization though a
little inconvenient to read. How about toJson() method? It might make the mllib
api inconsistent across models though.
You should probably create a JIRA for this.
CC: dev list
-Manish
> On Aug 26, 2015,
Congratulations Cheng, Joseph and Sean.
On Tuesday, February 3, 2015, Zhan Zhang wrote:
> Congratulations!
>
> On Feb 3, 2015, at 2:34 PM, Matei Zaharia > wrote:
>
> > Hi all,
> >
> > The PMC recently voted to add three new committers: Cheng Lian, Joseph
> Bradley and Sean Owen. All three have
ot. Is there a design doc explaining how the gradient boosting algorithm
> is laid out in MLLib? I tried reading the code, but without a "Rosetta
> stone" it's impossible to make sense of it.
>
> Alex
>
> On Mon, Nov 17, 2014 at 8:25 PM, Manish Amde wrote:
>
>>
;weak
> hypothesis weights". Does this refer to the weights of the leaves of the
> trees?
>
> Alex
>
> On Mon, Nov 17, 2014 at 2:24 PM, Manish Amde > wrote:
>
>> Hi Alessandro,
>>
>> MLlib v1.1 supports variance for regression and gini impurity
Hi Alessandro,
MLlib v1.1 supports variance for regression and gini impurity and entropy
for classification.
http://spark.apache.org/docs/latest/mllib-decision-tree.html
If the information gain calculation can be performed by distributed
aggregation then it might be possible to plug it into the e
Sean, sorry for missing out on the discussion.
Evan, you are correct, we are using the heuristic Sean suggested during the
multiclass PR for ordering high-arity categorical variables using the
impurity values for each categorical feature.
Joseph, thanks for fixing the bug which I think was a regr
: https://www.linkedin.com/in/dbtsai
> On Sun, May 4, 2014 at 1:12 AM, Manish Amde wrote:
>> I am currently using the RDD aggregate operation to reduce (fold) per
>> partition and then combine using the RDD aggregate operation.
>> def aggregate[U: ClassTag](zeroValue: U)(seqO
I am currently using the RDD aggregate operation to reduce (fold) per
partition and then combine using the RDD aggregate operation.
def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U)
=> U): U
I need to perform a transform operation after the seqOp and before the
combOp. Th
