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Categorical cross-entropy is the standard loss function for classifying into several mutually exclusive classes. It compares the distribution the network predicts, usually after a softmax, with the true label written in one-hot format, and it penalises heavily any low probability assigned to the correct class.
The Softmax function transforms a neural network's output logits into a probability distribution that sums to 1. It is the standard activation for multi-class classification, from image classifiers to the vocabulary layer of language models like GPT.
LazyPredict is a Python library that automatically evaluates dozens of scikit-learn classification and regression models on your dataset in seconds, without writing training code for each one. LazyClassifier and LazyRegressor return a comparative metrics table that shows which models are worth tuning further.
