# Imbalanced dataset - Positive majority class

My dataset consists of 150 patients where 50 are controls/healthy (negative) and 100 are sick (positive). If I want my model to have high sensitivity at hight specificity, in other words to have low false positive rates, should I correct my model by applying weights to it? Because usually the positive class is the minority class and I see why you need to correct for it but should I in my case?

Thanks

1) If you use a classifier $$f$$ that returns a value $$f(x)\in[0,1]$$ between 0 and 1 instead of a direct class assignment, then you can use a threshold $$\theta\in[0,1]$$:
$$f(x)=\begin{cases} 1,& \text{if } f(x)\geq\theta\\ 0, & \text{else } \end{cases}$$
The higher the value of $$\theta$$, the more you want to be sure to classify somebody as positive, which means that there will be fewer false positives, but at the same time you will have a fewer sensitivity (recall) because there will be more false negatives.