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Understanding Deep Learning Applications in Rare Event Prediction

Refining Neural Networks with Class Weights

Discover how adjusting class weights in neural network models can address the challenge of predicting rare events.

We'll cover the following...

Loss function

A rare event problem has very few positively labeled samples. Due to this, even if the classifier misclassifies the positive labels, their effect on the loss function is minuscule.

L(θ)=−1n∑i=1n(yilog(pi)+(1−yi)log(1−pi))\mathcal{L(\theta)}=−\frac{1}{n}\sum_{i=1}^{n}\big( y_{i}log(p_{i})+(1−y_{i})log(1−p_{i})\big)

where

  • yi∈0,1y_{i} ∈ 0,1 are the true labels.
  • pi=Pr[yi=1
...