We have successfully completed the ordinal encoding process ,Now input data i.e X_train & X_test set is ready to fit in any ML model. #Now import the LaberEncoder from sklearn to perform Label encodingfromsklear
Now we will use OnHotEncoder to encode other variables,then feed the data to our model. one=OneHotEncoder() one.fit(X_train_cyclic) train=one.transform(X_train_cyclic) print('train data set has got {} rows and {} columns'.format(train.shape[0],train.shape[1])) train data set ha...
resulting in 767 IDH-mutant specimens and 659 IDH-wild-type specimens. FastGlioma achieved a mean AUROC of 92.1 ± 0.9% for differentiating the four degrees of diffuse glioma infiltration (Fig.2a). Normalized infiltration scores were strongly correlated with ground-truth ordinal labels, with ...