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Dec 5, 2013 at 16:00 vote accept user35581
Dec 4, 2013 at 23:41 answer added user4673 timeline score: 0
Dec 4, 2013 at 23:18 answer added JEquihua timeline score: 1
Dec 4, 2013 at 23:10 vote accept user35581
Dec 5, 2013 at 16:00
Dec 4, 2013 at 21:15 comment added Vojta Rylko Can you provide a data sample and settings/script of the experiment?
Dec 4, 2013 at 16:58 comment added user35581 Also, the probability of a hit should never really exceed ~0.3, which would still be a probable miss, but that's ok.
Dec 4, 2013 at 16:51 comment added user35581 ~1000 datapoints, and ~100 1s. Maybe I wasn't understanding your definition of unbalanced, but if you're saying that there are far more 0s than 1s, then yes, there are, but there is still enough data for a random forest classifier model. The data set is unbalanced by design, as the results will be unbalanced.
Dec 4, 2013 at 16:04 comment added JEquihua I concur with @Vojtech R. this is probably due to an imbalanced target variable (e.g. 10% 1's and 90% 0's) How many cases do you have of each (1's and 0's)? Another thing that could be happening is that your independent variables don't explain your target very well.
Dec 4, 2013 at 13:42 answer added Marc Claesen timeline score: 1
Dec 4, 2013 at 13:40 comment added user35581 The training set is balanced. The problem is that the hit rate is so infrequent that a classifier would always predict a miss if forced to make a binary decision. If your random-forest classifier can also output probabilities in the prediction, it's much more meaningful.
Dec 4, 2013 at 13:37 answer added user35581 timeline score: 0
Dec 3, 2013 at 22:31 history edited user88 CC BY-SA 3.0
edited body; edited title
Dec 3, 2013 at 20:53 comment added Vojta Rylko It sounds like you have unbalanced traning data set. Can you provide frequency of the miss and hit classes in training data?
Dec 3, 2013 at 20:31 review First posts
Dec 3, 2013 at 20:33
Dec 3, 2013 at 20:13 history asked user35581 CC BY-SA 3.0