Monte Carlo Dropout for Uncertainty Estimation in Deep Learning Model

Monte Carlo Dropout is an advanced neural network method that leverages dropout regularization to produce more reliable predictions and estimate prediction uncertainties. Regularization techniques, such as dropout, are utilized in deep learning models to prevent overfitting. Dropout works by randomly removing nodes or neurons during training, which simplifies the model and prevents it from memorizing the training data. However, Monte Carlo Dropout goes beyond the traditional use of dropout in training and extends it to the inference…