Subset of training set produces good results while full training set produces poor results I have an extremely unbalanced data set: around 200 positive samples and 70,000 negative samples. To overcome this problem I have tried to over-sample the minority class as suggested in previous questions here.

Since training the classifier (in my case I am using SVM) takes a long time on my computer I first trained a classifier on a small subset of my data picked randomly, the classifier gave solid results when tested on the test set, yet when I trained a classifier using the full training data the classifier produced very poor results. Does anyone have insight into why this is happening and what should i do? I have also tried using weights but it did not produce a different result.
For the 200 positive samples, are there many different "types"? In other words, will future positive samples look nothing like the current positive samples? If this is the case, you may try anomaly detection in machine learning. 

From a lecture by Andrew Ng in Coursera, if you have small number of positive samples (e.g., 20), large number of negative samples (e.g., 10000), and it's hard for any algorithm to learn from the positive samples, you should use anomaly detection, otherwise, you can use supervised learning.