In the following code, class weights are tuned to see the performance change in decision trees with the same parameters. A dummy DataFrame is created to save all the results of various precision-recall details of combinations:
>>> dummyarray = np.empty((6,10)) >>> dt_wttune = pd.DataFrame(dummyarray)
Metrics to be considered for capture are weight for zero and one category (for example, if the weight for zero category given is 0.2, then automatically, weight for the one should be 0.8, as total weight should be equal to 1), training and testing accuracy, precision for zero category, one category, and overall. Similarly, recall for zero category, one category, and overall are also calculated:
>>> dt_wttune.columns = ["zero_wght","one_wght","tr_accuracy", "tst_accuracy", "prec_zero","prec_one", "prec_ovll", "recl_zero","recl_one","recl_ovll"]Weights for the zero category are verified from 0.01 to 0.5, as we know we do...