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 Looking at the docs for that algo, 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]> 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]> 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 > -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 > > > > >
