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author | Yuhao Yang <hhbyyh@gmail.com> | 2015-07-01 11:17:56 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-07-01 11:17:56 -0700 |
commit | 2012913355993e6516e4c81dbc92e579977131da (patch) | |
tree | ebb302ea581314397e679ba5d8733465d5f03272 /docs/mllib-collaborative-filtering.md | |
parent | 184de91d15a4bfc5c014e8cf86211874bba4593f (diff) | |
download | spark-2012913355993e6516e4c81dbc92e579977131da.tar.gz spark-2012913355993e6516e4c81dbc92e579977131da.tar.bz2 spark-2012913355993e6516e4c81dbc92e579977131da.zip |
[SPARK-8308] [MLLIB] add missing save load for python example
jira: https://issues.apache.org/jira/browse/SPARK-8308
1. add some missing save/load in python examples. , LogisticRegression, LinearRegression and NaiveBayes
2. tune down iterations for MatrixFactorization, since current number will trigger StackOverflow for default java configuration (>1M)
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #6760 from hhbyyh/docUpdate and squashes the following commits:
9bd3383 [Yuhao Yang] update scala example
8a44692 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into docUpdate
077cbb8 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into docUpdate
3e948dc [Yuhao Yang] add missing save load for python example
Diffstat (limited to 'docs/mllib-collaborative-filtering.md')
-rw-r--r-- | docs/mllib-collaborative-filtering.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index dfdf6216b2..eedc23424a 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -77,7 +77,7 @@ val ratings = data.map(_.split(',') match { case Array(user, item, rate) => // Build the recommendation model using ALS val rank = 10 -val numIterations = 20 +val numIterations = 10 val model = ALS.train(ratings, rank, numIterations, 0.01) // Evaluate the model on rating data @@ -149,7 +149,7 @@ public class CollaborativeFiltering { // Build the recommendation model using ALS int rank = 10; - int numIterations = 20; + int numIterations = 10; MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01); // Evaluate the model on rating data @@ -210,7 +210,7 @@ ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l # Build the recommendation model using Alternating Least Squares rank = 10 -numIterations = 20 +numIterations = 10 model = ALS.train(ratings, rank, numIterations) # Evaluate the model on training data |