[SPARK-7568][ML] ml.LogisticRegression doesn't output the right prediction #6109
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asfgit
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May 14, 2015
…iction The difference is because we previously don't fit the intercept in Spark 1.3. Here, we change the input `String` so that the probability of instance 6 can be classified as `1.0` without any ambiguity. with lambda = 0.001 in current LOR implementation, the prediction is ``` (4, spark i j k) --> prob=[0.1596407738787411,0.8403592261212589], prediction=1.0 (5, l m n) --> prob=[0.8378325685476612,0.16216743145233883], prediction=0.0 (6, spark hadoop spark) --> prob=[0.0692663313297627,0.9307336686702373], prediction=1.0 (7, apache hadoop) --> prob=[0.9821575333444208,0.01784246665557917], prediction=0.0 ``` and the training accuracy is ``` (0, a b c d e spark) --> prob=[0.0021342419881406746,0.9978657580118594], prediction=1.0 (1, b d) --> prob=[0.9959176174854043,0.004082382514595685], prediction=0.0 (2, spark f g h) --> prob=[0.0014541569986711233,0.9985458430013289], prediction=1.0 (3, hadoop mapreduce) --> prob=[0.9982978367343561,0.0017021632656438518], prediction=0.0 ``` Author: DB Tsai <dbt@netflix.com> Closes #6109 from dbtsai/lor-example and squashes the following commits: ac63ce4 [DB Tsai] first commit (cherry picked from commit c1080b6) Signed-off-by: Xiangrui Meng <meng@databricks.com>
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LGTM. Merged into master and branch-1.4. Thanks! |
jeanlyn
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May 28, 2015
…iction The difference is because we previously don't fit the intercept in Spark 1.3. Here, we change the input `String` so that the probability of instance 6 can be classified as `1.0` without any ambiguity. with lambda = 0.001 in current LOR implementation, the prediction is ``` (4, spark i j k) --> prob=[0.1596407738787411,0.8403592261212589], prediction=1.0 (5, l m n) --> prob=[0.8378325685476612,0.16216743145233883], prediction=0.0 (6, spark hadoop spark) --> prob=[0.0692663313297627,0.9307336686702373], prediction=1.0 (7, apache hadoop) --> prob=[0.9821575333444208,0.01784246665557917], prediction=0.0 ``` and the training accuracy is ``` (0, a b c d e spark) --> prob=[0.0021342419881406746,0.9978657580118594], prediction=1.0 (1, b d) --> prob=[0.9959176174854043,0.004082382514595685], prediction=0.0 (2, spark f g h) --> prob=[0.0014541569986711233,0.9985458430013289], prediction=1.0 (3, hadoop mapreduce) --> prob=[0.9982978367343561,0.0017021632656438518], prediction=0.0 ``` Author: DB Tsai <dbt@netflix.com> Closes apache#6109 from dbtsai/lor-example and squashes the following commits: ac63ce4 [DB Tsai] first commit
jeanlyn
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Jun 12, 2015
…iction The difference is because we previously don't fit the intercept in Spark 1.3. Here, we change the input `String` so that the probability of instance 6 can be classified as `1.0` without any ambiguity. with lambda = 0.001 in current LOR implementation, the prediction is ``` (4, spark i j k) --> prob=[0.1596407738787411,0.8403592261212589], prediction=1.0 (5, l m n) --> prob=[0.8378325685476612,0.16216743145233883], prediction=0.0 (6, spark hadoop spark) --> prob=[0.0692663313297627,0.9307336686702373], prediction=1.0 (7, apache hadoop) --> prob=[0.9821575333444208,0.01784246665557917], prediction=0.0 ``` and the training accuracy is ``` (0, a b c d e spark) --> prob=[0.0021342419881406746,0.9978657580118594], prediction=1.0 (1, b d) --> prob=[0.9959176174854043,0.004082382514595685], prediction=0.0 (2, spark f g h) --> prob=[0.0014541569986711233,0.9985458430013289], prediction=1.0 (3, hadoop mapreduce) --> prob=[0.9982978367343561,0.0017021632656438518], prediction=0.0 ``` Author: DB Tsai <dbt@netflix.com> Closes apache#6109 from dbtsai/lor-example and squashes the following commits: ac63ce4 [DB Tsai] first commit
nemccarthy
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Jun 19, 2015
…iction The difference is because we previously don't fit the intercept in Spark 1.3. Here, we change the input `String` so that the probability of instance 6 can be classified as `1.0` without any ambiguity. with lambda = 0.001 in current LOR implementation, the prediction is ``` (4, spark i j k) --> prob=[0.1596407738787411,0.8403592261212589], prediction=1.0 (5, l m n) --> prob=[0.8378325685476612,0.16216743145233883], prediction=0.0 (6, spark hadoop spark) --> prob=[0.0692663313297627,0.9307336686702373], prediction=1.0 (7, apache hadoop) --> prob=[0.9821575333444208,0.01784246665557917], prediction=0.0 ``` and the training accuracy is ``` (0, a b c d e spark) --> prob=[0.0021342419881406746,0.9978657580118594], prediction=1.0 (1, b d) --> prob=[0.9959176174854043,0.004082382514595685], prediction=0.0 (2, spark f g h) --> prob=[0.0014541569986711233,0.9985458430013289], prediction=1.0 (3, hadoop mapreduce) --> prob=[0.9982978367343561,0.0017021632656438518], prediction=0.0 ``` Author: DB Tsai <dbt@netflix.com> Closes apache#6109 from dbtsai/lor-example and squashes the following commits: ac63ce4 [DB Tsai] first commit
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The difference is because we previously don't fit the intercept in Spark 1.3. Here, we change the input
Stringso that the probability of instance 6 can be classified as1.0without any ambiguity.with lambda = 0.001 in current LOR implementation, the prediction is
and the training accuracy is