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Which is an example of a skewed class distribution?
A challenge for beginners working with imbalanced classification problems is what a specific skewed class distribution means. For example, what is the difference and implication for a 1:10 vs. a 1:100 class ratio?
What does it mean to have an imbalanced classification problem?
An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is not equal. A challenge for beginners working with imbalanced classification problems is what a specific skewed class distribution means.
How to define the proportions of a class distribution?
A class distribution can be defined as a dictionary where the key is the class value (e.g. 0 or 1) and the value is the number of randomly generated examples to include in the dataset. For example, an equal class distribution with 5,000 examples in each class would be defined as:… # define the class distribution proportions = {0:5000, 1:5000} 1
What is the impact of scaling and shifting random?
If you multiply your x by 2 and want to keep your area constant, then x*y = 12*y = 24 => y = 24/12 = 2. Scaling the x by 2 = scaling the y by 1/2. If you didn’t scale down your y-axis, then your cumulative probabilities will be >1, which is not possible.
How is accuracy affected by a skewed classification?
Accuracy on the other hand was not sensitive to imbalance and presented a false cosy picture of good performance. This happens because as skew increases, predicting the most frequent class will give high accuracy. In a 1:99 skew case (1 Positive and 99 negative examples) if you predict negative always, then you are 99% accurate.
Can you use anomaly detection on skewed classes?
However, anomaly detection cannot be applied to multiclass classification settings with skewed classes. The algorithm would only be able to tell which of the data records don’t belong to any of the labeled classes and therefore should be classified as something like “other”.
Which is the correct TPR for a skewed classification?
As imbalance increases, TPR will mostly remain constant since it depends on misclassifying Positive examples. If our algorithm has 90% detection then TPR = 90/ (90+10) = 0.9, i.e TPR doesn’t depend on skew but rather on how well our algorithm can detect the positive class only.