Description: Negative Log Likelihood (NLL) is a loss function widely used in classification tasks, especially in supervised learning problems. Its main goal is to measure the discrepancy between the model’s predictions and the actual labels of the data. In simple terms, NLL evaluates how well a probabilistic model fits the observed data, penalizing incorrect predictions more severely as the probability assigned to the correct class decreases. This function is based on probability theory and is derived from the likelihood function, where the aim is to maximize the probability of observing the data given the model parameters. In the context of machine learning, NLL is commonly used alongside activation functions like Softmax, which converts the model’s outputs into probabilities. A key feature of NLL is that it is non-negative, meaning it always produces zero or positive values, and its minimum value is reached when the model predicts the correct class with certainty. This property makes it a valuable tool for training classification models, as it provides a clear signal for optimization during the learning process.
Uses: Negative Log Likelihood is primarily used in classification problems, such as image classification, natural language processing, and fraud detection. It is particularly useful in models that generate probabilities, such as deep learning models, where a precise assessment of prediction quality is required. Additionally, it is applied in logistic regression models and in machine learning algorithms that require a loss function that reflects the probability of classes.
Examples: A practical example of Negative Log Likelihood is its use in image classification, where a convolutional neural network (CNN) model can be trained to identify different objects in images. By using NLL as the loss function, the model adjusts its parameters to maximize the probability of correctly classifying the images. Another example is found in natural language processing, where NLL is used to train language models that predict the next word in a given sequence, thereby optimizing the accuracy of the predictions.
