Hi, We have one use case in which we need to handle the categorical variables in SVM, Regression and Logistic regression models(MLlib not ML) for scoring.
We are getting the possible category values against each category variable. So how the string value of categorical variable can be converted into double values for forming the features vector ? Also how the weight for individual categories can be calculated for models. Like we have Gender as variable with categories as Male and Female and we want to give more weight to female category, then how this can be accomplished? Also is there a way through which string values from raw text can be converted to features vector(Apart from the HashingTF-IDF transformation) ? -- Thanks and Regards, Hokam Singh Chauhan Mobile : 09407125190 -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/How-to-handle-categorical-variables-in-Spark-MLlib-tp25767.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
