Thank you for posting this, I was just having a look at the notebook and I see that the score for multi_target_forest.score(features, labels_n) is 0.226 on the training set. Do these trained SVM model give a 0.7027 on the test set? I ran the code myself aswell and found that the accuracy for each respective label is roughly 0.5 : 0.8: 0.5.
Im struggling to understand how this gets 0.7027 on the test set. I might be missing somthing, could you perhaps help me understand this and point me in the right direction?
I do not really have the understanding of the data.In order to create training data i simpy averaged the data from 60 readings of same donation id. After that i simply applied some machine learning model simultaneously for all three output as given in starter notebook.
Among all the model i used ,SVM achieved best over all accuracy
on public test set.
Hi Suman-123,
Thank you for posting this, I was just having a look at the notebook and I see that the score for multi_target_forest.score(features, labels_n) is 0.226 on the training set. Do these trained SVM model give a 0.7027 on the test set? I ran the code myself aswell and found that the accuracy for each respective label is roughly 0.5 : 0.8: 0.5.
Im struggling to understand how this gets 0.7027 on the test set. I might be missing somthing, could you perhaps help me understand this and point me in the right direction?
Hy DylanGeldenhuys,
I do not really have the understanding of the data.In order to create training data i simpy averaged the data from 60 readings of same donation id. After that i simply applied some machine learning model simultaneously for all three output as given in starter notebook.
Among all the model i used ,SVM achieved best over all accuracy on public test set.