I have a patient data named dat and labels (0 = No Disease, 1 = Disease) named labl both in the form of array. I predicted my model and stored the predictions named pre which is also an array, and I want to calculate and plot the AUC ROC. But I am getting this error while doing so. TypeError: Singleton
Tag: auc
How to change plot legends with roc_auc_score?
I’m plotting ROC curve with plot_roc_curve of scikit-learn, that plot legends are printed automatically. Is there a way to change them? Answer You can pretty much add anything you like to the plot object that is produced through plot_roc_curve. For instance, you can do something like this: This will return:
tensorflow: InvalidArgumentError while find the AUC score
I have a dataset with labels 0’s and 1’s, which is binary classification problem. Getting error while try to find AUC score using tf.keras.metrics.AUC() as metrics in model.compile(.. function. Code: If I removed AUC from metrics, the code runs fine. Error: InvalidArgumentError: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (sequential_48/dense_293/BiasAdd:0) =
sklearn roc_auc_score with multi_class==”ovr” should have None average available
I’m trying to compute the AUC score for a multiclass problem using the sklearn’s roc_auc_score() function. I have prediction matrix of shape [n_samples,n_classes] and a ground truth vector of shape [n_samples], named np_pred and np_label respectively. What I’m trying to achieve is the set of AUC scores, one for each classes that I have. To do so I would like