# Huge False Negatives

Assuming class1 is positive and class0 negative, shouldn’t the weight be passed as the reciprocal?

From the docs:

For example, if a dataset contains 100 positive and 300 negative examples of a single class, then pos_weight for the class should be equal to 300/100=3. The loss would act as if the dataset contains 3 * 100=300 positive examples.

yeah tried reciprocal too… same results…

Could you try increasing it until the confusion matrix changes?

ok give some suggestions… anyway i will keep some for loop… what ranges should i give ?

Hi @ptrblck,

below are the weights i tested and there is no deviation in confusion matrix and one more observation is as weight value is increasing difference between validation loss and training loss is increasing. so is that i need to test for lower weights ? and what is the weight difference i should keep in for loop ? (value of 10 ?)

[[2.5],[2.55],[2.6],[2.65],[2.7],[2.75],[2.8],[2.85],[2.9],[2.95],[3],[3.05],[3.1],[3.15],[3.2],[3.25],[3.3],[3.35],[3.4],[3.45],[3.5],[3.55],[3.6],[3.65],[3.7],[3.75],[3.8],[3.85],[3.9],[3.94999999999999],[3.99999999999999],[4.04999999999999],[4.09999999999999],[4.14999999999999],[4.19999999999999],[4.24999999999999],[4.29999999999999],[4.34999999999999],[4.39999999999999],[4.44999999999999],[4.49999999999999],[4.54999999999999],[4.59999999999999],[4.64999999999999],[4.69999999999999],[4.74999999999999],[4.79999999999999],[4.84999999999999],[4.89999999999999],[4.94999999999999],[4.99999999999999],[5.04999999999999],[5.09999999999999],[5.14999999999999],[5.19999999999999],[5.24999999999999],[5.29999999999999],[5.34999999999999],[5.39999999999999],[5.44999999999999],[5.49999999999999],[5.54999999999999],[5.59999999999999],[5.64999999999999],[5.69999999999999],[5.74999999999999],[5.79999999999999],[5.84999999999999],[5.89999999999999],[5.94999999999999],[5.99999999999999],[6.04999999999999],[6.09999999999999],[6.14999999999999],[6.19999999999999],[6.24999999999999],[6.29999999999999],[6.34999999999999],[6.39999999999999],[6.44999999999999],[6.49999999999999],[6.54999999999999],[6.59999999999999],[6.64999999999999],[6.69999999999999],[6.74999999999998],[6.79999999999998],[6.84999999999998],[6.89999999999998],[6.94999999999998],[6.99999999999998],[7.04999999999998],[7.09999999999998],[7.14999999999998],[7.19999999999998],[7.24999999999998],[7.29999999999998],[7.34999999999998],[7.39999999999998],[7.44999999999998],[7.49999999999998],[7.54999999999998],[7.59999999999998],[7.64999999999998],[7.69999999999998],[7.74999999999998],[7.79999999999998],[7.84999999999998],[7.89999999999998],[7.94999999999998],[7.99999999999998],[8.04999999999998],[8.09999999999998],[8.14999999999998],[8.19999999999998],[8.24999999999998],[8.29999999999998],[8.34999999999998],[8.39999999999998],[8.44999999999998],[8.49999999999998],[8.54999999999998],[8.59999999999998],[8.64999999999998],[8.69999999999998],[8.74999999999998],[8.79999999999998],[8.84999999999998],[8.89999999999998],[8.94999999999998],[8.99999999999998],[9.04999999999998],[9.09999999999998],[9.14999999999998],[9.19999999999998],[9.24999999999998],[9.29999999999998],[9.34999999999998],[9.39999999999998],[9.44999999999998],[9.49999999999998],[9.54999999999997],[9.59999999999997],[9.64999999999997],[9.69999999999997],[9.74999999999997],[9.79999999999997],[9.84999999999997],[9.89999999999997],[9.94999999999997],[9.99999999999997],[10.05],[10.1],[10.15],[10.2],[10.25],[10.3],[10.35],[10.4],[10.45],[10.5],[10.55],[10.6],[10.65],[10.7],[10.75],[10.8],[10.85],[10.9],[10.95],[11],[11.05],[11.1],[11.15],[11.2],[11.25],[11.3],[11.35],[11.4],[11.45],[11.5],[11.55],[11.6],[11.65],[11.7],[11.75],[11.8],[11.85],[11.9],[11.95],[12],[12.05],[12.1],[12.15]
]