Binary classification with strongly unbalanced classes
Oct 30, 2017 · Here's the abstract: We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or …
How would I bias my binary classifier to prefer false positive errors ...
Jan 24, 2021 · 15 I've put together a binary classifier using Keras' Sequential model. Of its errors, it predicts with false negatives more frequently than false positives. This tool is for medical application, …
A binary classifier with accuracy below 50% and similar situations
Apr 5, 2023 · 1 Let's say, I have a binary classifier. Usually, if a model doesn't learn anything useful, the accuracy would be around 50%. Anything above 50 is better than a random guess. My question is: …
Many binary classifiers vs. single multiclass classifier
What factors should be considered when determining whether to use multiple binary classifiers or a single multiclass classifier? For example, I'm building a model that does hand gesture classification.
Neural Network: For Binary Classification use 1 or 2 output neurons?
Assume I want to do binary classification (something belongs to class A or class B). There are some possibilities to do this in the output layer of a neural network: Use 1 output node. Output 0 (&l...
classification - What is the difference between a multiclass and a ...
Jun 26, 2023 · Turning a "binary classifier" into a "multiclass classifier" is possible using "One-vs-One" or "One-vs-Rest" strategies, discussed on the scikit-learn and elsewhere. Note: ... all classifiers …
Making a multiclass classification problem binary during data ...
Dec 8, 2023 · To me, binary classification is more intuitive. Such a classifier must only "know" how a good part looks like, while the multi class one needs to learn all the different anomalies.
Recall and precision in classification - Cross Validated
In the context of binary classification, examples are either positive or negative. The recall addresses the question: "Given a positive example, will the classifier detect it ?"
How to prepare a 2x2 Confusion matrix for binary classifier
Apr 7, 2021 · Problem statement: Evaluate a binary classifier. There are 50 positive outcomes in the test data, and 100 observations. Using a 50% threshold, the classifier predicts 40 positive outcomes, of …
classification - threshold choice for binary classifier: on training ...
May 31, 2020 · I have a binary classification problem where I perform cross validation on the training set (currently 80% of the examples) and then evaluate results on a test set. I use cross validation for find...