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DB Tsai resolved SPARK-10231.
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Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 8421
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https://issues.apache.org/jira/browse/SPARK-10244?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai resolved SPARK-10244.
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Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 8430
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DB Tsai resolved SPARK-10239.
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Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 8430
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DB Tsai updated SPARK-7780:
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Target Version/s: 1.6.0 (was: 1.5.0)
The intercept in LogisticRegressionWithLBFGS should not be regularized
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DB Tsai resolved SPARK-8916.
Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 7518
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https://issues.apache.org/jira/browse/SPARK-9642?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai updated SPARK-9642:
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Labels: 1.6 (was: )
LinearRegression should supported weighted data
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DB Tsai reassigned SPARK-9612:
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Assignee: DB Tsai
Add instance weight support for GBTs
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DB Tsai resolved SPARK-8522.
Resolution: Fixed
Disable feature scaling in Linear and Logistic Regression
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DB Tsai commented on SPARK-6683:
I think we can close this one. They are addressed in
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DB Tsai resolved SPARK-7518.
Resolution: Won't Fix
Tests for comparing probability and prediction in binary logistic regression
DB Tsai created SPARK-9442:
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Summary: java.lang.ArithmeticException: / by zero when reading
Parquet
Key: SPARK-9442
URL: https://issues.apache.org/jira/browse/SPARK-9442
Project: Spark
Issue Type:
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DB Tsai commented on SPARK-9442:
I will try to turn on the logging at the info level.
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DB Tsai edited comment on SPARK-9442 at 7/29/15 8:28 PM:
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Another
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DB Tsai updated SPARK-9442:
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Description:
I am counting how many records in my nested parquet file with this schema,
{code}
scala
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DB Tsai updated SPARK-9442:
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Description:
I am counting how many records in my nested parquet file with this schema,
{code}
scala
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DB Tsai commented on SPARK-9442:
Another note: By explicitly looping through the data to
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DB Tsai updated SPARK-9442:
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Description:
I am counting how many records in my nested parquet file with this schema,
{code}
scala
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DB Tsai updated SPARK-9442:
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Description:
I am counting how many records in my nested parquet file with this schema,
{code}
scala
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DB Tsai reassigned SPARK-7685:
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Assignee: DB Tsai (was: Shuo Xiang)
Handle high imbalanced data and apply weights to different
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https://issues.apache.org/jira/browse/SPARK-9204?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai resolved SPARK-9204.
Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 7553
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https://issues.apache.org/jira/browse/SPARK-8913?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai resolved SPARK-8913.
Resolution: Fixed
Follow-up on SPARK-8700. Cleanup the test
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DB Tsai commented on SPARK-8913:
merged https://github.com/apache/spark/pull/7335
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DB Tsai updated SPARK-8913:
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Fix Version/s: 1.5.0
Follow-up on SPARK-8700. Cleanup the test
-
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DB Tsai resolved SPARK-8963.
Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 7327
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DB Tsai updated SPARK-8963:
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Assignee: holdenk
Improve Linear Regression tests to use Vectors
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DB Tsai resolved SPARK-8700.
Resolution: Fixed
Issue resolved by pull request 7080
[https://github.com/apache/spark/pull/7080]
Disable
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DB Tsai reassigned SPARK-7159:
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Assignee: DB Tsai
Support multiclass logistic regression in spark.ml
DB Tsai created SPARK-8912:
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Summary: Follow-up on SPARK-8700. Documentation and cleanup the
test
Key: SPARK-8912
URL: https://issues.apache.org/jira/browse/SPARK-8912
Project: Spark
Issue Type:
DB Tsai created SPARK-8913:
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Summary: Follow-up on SPARK-8700. Cleanup the test
Key: SPARK-8913
URL: https://issues.apache.org/jira/browse/SPARK-8913
Project: Spark
Issue Type: Improvement
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DB Tsai updated SPARK-8912:
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Description: We need to clearly document the effectively objective function
when `standardization` is
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DB Tsai updated SPARK-8912:
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Summary: Follow-up on SPARK-8700. Documentation (was: Follow-up on
SPARK-8700. Documentation and cleanup the
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DB Tsai updated SPARK-8913:
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Assignee: holdenk
Follow-up on SPARK-8700. Cleanup the test
-
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DB Tsai updated SPARK-8912:
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Summary: Documentation on the effective objective function in LoR when
`standardization` is true/false (was:
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DB Tsai updated SPARK-8912:
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Description:
Follow-up on SPARK-8700. Documentation
We need to clearly document the effectively objective
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DB Tsai updated SPARK-8912:
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Description:
This is a follow-up PR to https://issues.apache.org/jira/browse/SPARK-8700
which will document
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DB Tsai commented on SPARK-8849:
I hope that we can port MLOR to ML pipeline framework in
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DB Tsai closed SPARK-2505.
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Resolution: Won't Fix
Weighted Regularizer
Key: SPARK-2505
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DB Tsai resolved SPARK-8551.
Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 6946
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DB Tsai updated SPARK-8551:
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Assignee: Shuo Xiang
Python example code for elastic net
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DB Tsai created SPARK-8700:
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Summary: Disable feature scaling in Logistic Regression
Key: SPARK-8700
URL: https://issues.apache.org/jira/browse/SPARK-8700
Project: Spark
Issue Type: Sub-task
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DB Tsai reassigned SPARK-8700:
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Assignee: DB Tsai
Disable feature scaling in Logistic Regression
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DB Tsai reassigned SPARK-8522:
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Assignee: DB Tsai (was: holdenk)
Disable feature scaling in Linear and Logistic Regression
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DB Tsai resolved SPARK-8613.
Resolution: Fixed
Assignee: holdenk
Fix Version/s: 1.5.0
Target Version/s:
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DB Tsai updated SPARK-8522:
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Comment: was deleted
(was: Issue resolved by pull request 7024
[https://github.com/apache/spark/pull/7024])
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DB Tsai updated SPARK-8601:
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Assignee: holdenk
Disable feature scaling in Linear Regression
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DB Tsai resolved SPARK-8522.
Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 7024
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DB Tsai reopened SPARK-8522:
Disable feature scaling in Linear and Logistic Regression
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DB Tsai closed SPARK-7888.
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Resolution: Done
Fix Version/s: 1.5.0
Target Version/s: 1.5.0
Be able to disable intercept in
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DB Tsai commented on SPARK-7888:
merged into master.
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https://issues.apache.org/jira/browse/SPARK-7674?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14596751#comment-14596751
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DB Tsai commented on SPARK-7674:
I mean should we have `trait` for shared properties like
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DB Tsai commented on SPARK-7674:
sounds fair.
How about the `estimate std error`, `t
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DB Tsai commented on SPARK-7674:
Also, will be nice to have like training confusion matrix
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DB Tsai updated SPARK-8522:
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Assignee: holdenk
Disable feature scaling in Linear and Logistic Regression
DB Tsai created SPARK-8522:
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Summary: Disable feature scaling in Linear and Logistic Regression
Key: SPARK-8522
URL: https://issues.apache.org/jira/browse/SPARK-8522
Project: Spark
Issue Type: New
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DB Tsai commented on SPARK-7456:
PR was already submitted.
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DB Tsai commented on SPARK-7456:
BTW, [~holdenk] did a benchmark comparing the
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DB Tsai commented on SPARK-7888:
Last night, I figured out how to do this. If you look at
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DB Tsai commented on SPARK-7888:
Yeah, we don't re-center but still scaling to unit
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DB Tsai commented on SPARK-7674:
How do we store those statistical metadata in model
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DB Tsai commented on SPARK-7674:
but lots of them share the same information. For example,
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DB Tsai updated SPARK-7888:
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Assignee: holdenk
Be able to disable intercept in Linear Regression in ML package
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DB Tsai updated SPARK-7685:
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Assignee: Shuo Xiang
Handle high imbalanced data and apply weights to different samples in
Logistic
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DB Tsai updated SPARK-7555:
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Assignee: Shuo Xiang (was: DB Tsai)
User guide update for ElasticNet
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DB Tsai resolved SPARK-8314.
Resolution: Fixed
Merged into master
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DB Tsai closed SPARK-8168.
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Resolution: Fixed
Issue resolved by pull request https://github.com/apache/spark/pull/6709
Add Python
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DB Tsai commented on SPARK-7008:
Do you see better convergence rate when LBFGS is used?
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DB Tsai commented on SPARK-7547:
Pong! Sorry for the delay. Was a busy weekend. Will send
DB Tsai created SPARK-7888:
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Summary: Be able to disable intercept in Linear Regression in ML
package
Key: SPARK-7888
URL: https://issues.apache.org/jira/browse/SPARK-7888
Project: Spark
Issue
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https://issues.apache.org/jira/browse/SPARK-7852?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai updated SPARK-7852:
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Issue Type: New Feature (was: Bug)
Set the initial weights based on the previous when GLMs are run with
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DB Tsai commented on SPARK-7888:
Sounds great. This requires some math to understand how R
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DB Tsai commented on SPARK-7547:
Sorry for delay. I can write Scala/Java example code by
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DB Tsai commented on SPARK-7780:
++1
Although I don't think there are many users
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DB Tsai commented on SPARK-7674:
I implemented the stats for ML models when I was Alpine,
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DB Tsai commented on SPARK-7780:
@holdenk Sure. This one will be fun to work on. If it's
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DB Tsai commented on SPARK-7555:
[~coderxiang] told me he will chime in and help out the
DB Tsai created SPARK-7780:
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Summary: The intercept in LogisticRegressionWithLBFGS should not
be regularized
Key: SPARK-7780
URL: https://issues.apache.org/jira/browse/SPARK-7780
Project: Spark
DB Tsai created SPARK-7685:
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Summary: Handle high imbalanced data or apply weights to different
samples in Logistic Regression
Key: SPARK-7685
URL: https://issues.apache.org/jira/browse/SPARK-7685
Project:
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DB Tsai updated SPARK-7685:
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Summary: Handle high imbalanced data and apply weights to different samples
in Logistic Regression (was:
DB Tsai created SPARK-7620:
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Summary: Removed calling size, length in while condition to avoid
extra JVM call
Key: SPARK-7620
URL: https://issues.apache.org/jira/browse/SPARK-7620
Project: Spark
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DB Tsai edited comment on SPARK-7568 at 5/13/15 6:06 AM:
-
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DB Tsai edited comment on SPARK-7568 at 5/13/15 6:05 AM:
-
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DB Tsai commented on SPARK-7568:
`fitIntercept = false`, or in Spark 1.3, the training
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DB Tsai commented on SPARK-7568:
Well, the third example is 0.0 in the old code.
```
(4,
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DB Tsai updated SPARK-7568:
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Comment: was deleted
(was: Oh... my bad. I guess you are referring the third example in the training
set.
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DB Tsai commented on SPARK-7568:
In 1.3,
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DB Tsai edited comment on SPARK-7568 at 5/13/15 5:37 AM:
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In 1.3,
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DB Tsai commented on SPARK-7568:
Default for R is true.
ml.LogisticRegression doesn't
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DB Tsai edited comment on SPARK-7568 at 5/13/15 5:43 AM:
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DB Tsai commented on SPARK-7568:
Actually, with lambda = 0.001, the training accuracy is
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DB Tsai commented on SPARK-7568:
This is because we regularize the intercept before which
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DB Tsai commented on SPARK-7568:
Oh... my bad. I guess you are referring the third example
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DB Tsai commented on SPARK-7547:
Sure! I'll add the example code and documentation in this
DB Tsai created SPARK-7518:
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Summary: Tests for comparing probability and prediction in binary
logistic regression against R
Key: SPARK-7518
URL: https://issues.apache.org/jira/browse/SPARK-7518
Project:
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https://issues.apache.org/jira/browse/SPARK-7262?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai updated SPARK-7262:
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Summary: Binary LogisticRegression with L1/L2 (elastic net) using OWLQN in
new ML package (was:
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DB Tsai updated SPARK-7262:
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Description:
1) Handle scaling and addBias internally.
2) L1/L2 elasticnet using OWLQN optimizer.
was:
1)
DB Tsai created SPARK-7279:
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Summary: Removed diffSum which is theoretical zero in
LinearRegression and coding formating
Key: SPARK-7279
URL: https://issues.apache.org/jira/browse/SPARK-7279
Project: Spark
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https://issues.apache.org/jira/browse/SPARK-7222?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai updated SPARK-7222:
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Description: Added detailed mathematical derivation of how scaling and
LeastSquaresAggregator work. Also
DB Tsai created SPARK-7222:
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Summary: Added mathematical derivation in comment to
LinearRegression with ElasticNet
Key: SPARK-7222
URL: https://issues.apache.org/jira/browse/SPARK-7222
Project: Spark
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DB Tsai updated SPARK-7222:
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Summary: Added mathematical derivation in comment and compressed the model
to LinearRegression with
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DB Tsai updated SPARK-7222:
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Issue Type: Improvement (was: Documentation)
Added mathematical derivation in comment and compressed the
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