Hello,
I am using linear SVM to train my model and generate a line through my data.
However my model always predicts 1 for all the feature examples. Here is my
code:

print data_rdd.take(5)
[LabeledPoint(1.0, [1.9643,4.5957]), LabeledPoint(1.0, [2.2753,3.8589]),
LabeledPoint(1.0, [2.9781,4.5651]), LabeledPoint(1.0, [2.932,3.5519]),
LabeledPoint(1.0, [3.5772,2.856])]

----------------------------------------------------------------------------------------
from pyspark.mllib.classification import SVMWithSGD
from pyspark.mllib.linalg import Vectors
from sklearn.svm import SVC
data_rdd=x_df.map(lambda x:LabeledPoint(x[1],x[0]))

model = SVMWithSGD.train(data_rdd, iterations=1000,regParam=1)

X=x_df.map(lambda x:x[0]).collect()
Y=x_df.map(lambda x:x[1]).collect()

----------------------------------------------------------------------------------------
pred=[]
for i in X:
  pred.append(model.predict(i))
print pred

[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1]


My dataset is as follows:
<http://apache-spark-user-list.1001560.n3.nabble.com/file/n27955/Screen_Shot_2016-10-25_at_2.png>
 


Can someone please help?



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