In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). Introduction When we develop a model for probabilistic classification, we aim to map the model's inputs to probabilistic predictions, and we often train our model by incrementally adjusting the model's parameters so that our predictions get closer and closer to ground-truth probabilities. Let’s explore this further by an example that was developed for Loan default cases. For model building, when we define the accuracy measures for the model, we look at optimizing the loss function. Bayes consistent loss functions: Zero-one loss (gray), Savage loss (green), Logistic loss (orange), Exponential loss (purple), Tangent loss (brown), Square loss (blue) Cross entropy as a loss function can be used for Logistic Regression and Neural networks.
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