Logptexttrueclass

Logptexttrueclass is a metric used in natural language processing to evaluate the performance of classification models on textual data. It quantifies the log‑probability assigned by a classifier to the correct class label for each input example. The metric is calculated by taking the negative logarithm of the probability that the model assigns to the true class. Lower values indicate a higher probability assigned to the true class, and thus a better-performing model.

The metric originates from probabilistic classification models such as logistic regression and feed‑forward neural networks that output probability distributions over a set of classes. By summing the log‑probabilities over all examples in a dataset, one obtains the total log‑likelihood of the true labels under the model. Dividing by the number of examples yields an average log‑p value. This value is directly related to cross‑entropy loss, which is the negative of the average log‑p. Therefore, logptexttrueclass can be interpreted as a normalized cross‑entropy specific to the true class and is often reported alongside accuracy to provide a more nuanced view of model confidence.

In practice, logptexttrueclass is especially useful when comparing models on imbalanced datasets. Accuracy can be misleading if a majority class dominates, whereas logptexttrueclass rewards correct probabilistic predictions even for minority classes. Researchers also use it to calibrate models: a model with a low logptexttrueclass tends to produce more reliable probability estimates. The metric is straightforward to compute, requiring only the model’s predicted probabilities and the ground truth labels. It is featured in many evaluation scripts for text classification tasks and is supported by popular machine learning libraries such as scikit‑learn and TensorFlow.