On the Template Gallery there is a github link, you could try creating an issue there to get the author’s attention. I notice there is also a reference for a blog post too under “support” https://github.com/goliasz/pio-template-sr <https://github.com/goliasz/pio-template-sr>
The Gallery is for any template, not all are known about or used by people here and so the template gallery contains either a github link for issues or some other support link. The ones donated with PredictionIO point to this email list. You may become the only expert here. But template authors are the best ones to try first. On Jul 28, 2017, at 2:43 AM, Rasna Tomar <[email protected]> wrote: Hi Yes, I am using Survival Regression template from the PredictionIO site. I have understood quantile and prediction wrt. SR. Thanks On Thu, Jul 27, 2017 at 11:16 PM, Mars Hall <[email protected] <mailto:[email protected]>> wrote: Hi Rasna, Folks here tend to respond to things they know about… I guess we don't have a lot of experts for Survival Regression… I'll do my best to reply: Are you using the Survival Regression template from the PredictionIO site? If so, we can see the exact algorithm in use is AFTSurvivalRegression: https://github.com/goliasz/pio-template-sr/blob/master/src/main/scala/SRAlgorithm.scala#L47 <https://github.com/goliasz/pio-template-sr/blob/master/src/main/scala/SRAlgorithm.scala#L47> Looking at the docs for that algo, https://spark.apache.org/docs/latest/ml-classification-regression.html#survival-regression <https://spark.apache.org/docs/latest/ml-classification-regression.html#survival-regression> …the coefficients, intercept and scale are based on the model's fit to the training data. Once the PredictionIO engine is trained, these will be constant until trained with a different data set. The quantiles and prediction are the answers for each specific query (transform). If you're seeking a deeper understanding of the prediction, I'm sorry but I do not have that expertise! *Mars ( <> .. <> ) > On Jul 27, 2017, at 02:51, Rasna Tomar <[email protected] > <mailto:[email protected]>> wrote: > > Why there is no support for templates other than universal recommender.?? > > On Wed, Jul 19, 2017 at 3:20 PM, Rasna Tomar <[email protected] > <mailto:[email protected]>> wrote: > Hi All > > > I am using survival regression for predicting whether user will purchase in > next few days or not. > > I am getting results similar to as shown below - > > Sample query - > curl -i -X POST http://localhost:8000/queries.json > <http://localhost:8000/queries.json> > -H "Content-Type: application/json" -d '{"features":[1.560,-0.605]}' > > > Output - > { > "coefficients": [ > -0.2633608588194104, > 0.22152319227842276 > ], > "intercept": 2.6380946151040012, > "prediction": 5.718979487634966, > "quantiles": [ > 1.1603238947151593, > 4.995456010274735 > ], > "scale": 1.5472345574364683 > > } > > > For each user I am getting same values coefficients, intercept and scale, but > Quantile and prediction values are different? > What is the meaning of quantile and prediction here? > > Thanks > > > > >
