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"Some help when training a regression algorithm"
Contributor II
Hi dear rapid-i coommunity,
I am traying the rapidminer modeling to make a content-based recommender system. To do that i downloaded the movielens 100K dataset which have information about movies and ratings made by users to movies. The ratings have a range bettween 0 and 5 and the movies has genre information (action, commedy, etc). I am training a classifier using the user who has more ratings (uid= 405; Number of reviews= 737) for doing that i discretize the rating (good >= 3.5; bad < 3.5) but due that the user has a lot of more reviews with label bad the classifier (libSVM) predicts all labels as bad.
Then i used another strategy where i did stratified sampling (http://rapid-i.com/rapidforum/index.php/topic,2190.0.html) to make class labels even. I get the following results
true bad true good class precision
pre.bad
pre.good
class recall
I am traying the rapidminer modeling to make a content-based recommender system. To do that i downloaded the movielens 100K dataset which have information about movies and ratings made by users to movies. The ratings have a range bettween 0 and 5 and the movies has genre information (action, commedy, etc). I am training a classifier using the user who has more ratings (uid= 405; Number of reviews= 737) for doing that i discretize the rating (good >= 3.5; bad < 3.5) but due that the user has a lot of more reviews with label bad the classifier (libSVM) predicts all labels as bad.
Then i used another strategy where i did stratified sampling (http://rapid-i.com/rapidforum/index.php/topic,2190.0.html) to make class labels even. I get the following results
true bad true good class precision
pre.bad
pre.good
class recall
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