Is it acceptable to weight by result in a Logistic Regression Model (Spark) I'm getting my head aroud Logistic Regression models. My data has a very low success rate (&lt; 0.1%), but the cost of false positives is tiny compared to the cost of a false negative, so I am happy to accept false positives as long as the model predicts as many of the positives correctly. 

So using the ML Logistic Regression model I have set the weight for positives to be 200 times the weight of negatives. The results are really encouraging, but I want to check this is the correct approach. 

My success rate feels almost too good this way: 

* 99.1% of positives are correctly predicted. 
* only 0.3% of samples are predicted to be positive, meaning I only have two false positives for every correct one. 

Without the weights, the result was closer to 20% of positives correctly predicted, but far fewer false positives, the cost of which is minimal. 