So you will need to convert your input DataFrame into something with vectors and labels to train on - the Spark ML documentation has examples http://spark.apache.org/docs/latest/ml-guide.html (although the website seems to be having some issues mid update to Spark 2.0 so if you want to read it right now http://spark.apache.org/docs/1.6.2/ml-guide.html#example-pipeline )
Hello,Question 1: I am new to Spark. I am trying to train classification model on Spark DataFrame. I am using PySpark. And aFrame object in df:ted a Spark DataFrame object in df:from pyspark.sql.types import *
query = """select * from table"""
df = sqlContext.sql(query)My question is how to continue extend the code to train models (e.g., classification model etc.) on object df? I have checked many online resources and haven't seen any similar approach like the following:lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) # Fit the model lrModel = lr.fit(df)Is it a feasible way to train the model? If yes, where could I find the reference code?Question 2: Why in MLib dataframe based API there is no SVM model support, however, in RDD-based APIs there was SVM model?Thanks a lot!Best,Shi