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The cross-entropy loss function works very well for models that predict binary classes (aka the output is between 0 and 1). It is defined as -[y*log(y-hat) +(1-y)*log(1-(y-hat))]. If y=0 the left side of the function is dropped and the right side, -log(1-(y-hat)), is used. Otherwise if y=1 the right side of the function is dropped and it uses -log(y-hat). In both instances this loss function encourages probabilities that are close to the true probability. 

Cross-entropy loss

Any loss consisting of a negative log-likelihood is a cross-entropy between the empirical distribution deﬁned by the training set and the probability distribution deﬁned by model.

A Broad Definition of Cross Entropy

1. A perfect classifier would assign probability 1 to the correct outcome (y=1 or y=0) and probability 0 to the incorrect outcome. That means if y equals 1, the higher ˆy is
(the closer it is to 1), the better the classifier; the lower y^ is (the closer it is to 0),
the worse the classifier. If y equals 0, instead, the higher 1 − y^ is (closer to 1), the
better the classifier. The negative log of ˆy (if the true y equals 1) or 1−y^ (if the true
y equals 0) is a convenient loss metric since it goes from 0 (negative log of 1, no
loss) to infinity (negative log of 0, infinite loss). 

2. This loss function also ensures that as the probability of the correct answer is maximized, the probability of the incorrect answer is minimized; since the two sum to one, any increase in the probability of the correct answer is coming at the expense of the incorrect answer. 

Why we want to minimize cross-entropy loss?

The training objective of a denoising autoencoder is to identify the optimal parameters for the encoder ($$\theta$$) and decoder ($$\omega$$) to minimize reconstruction error. During training, a corrupted input $$\mathbf{x}_{\mathrm{noise}}$$ is generated by adding noise to the original input $$\mathbf{x}$$. The model processes this noisy input, and the loss function—frequently chosen as cross-entropy loss—measures how effectively the decoder recovers the original $$\mathbf{x}$$. The objective to find the optimal parameters, $$\hat{\theta}$$ and $$\hat{\omega}$$, is mathematically defined as:

$$(\hat{\theta},\hat{\omega}) = \arg\min_{\theta,\omega} \mathrm{Loss}(\mathrm{Model}_{\theta,\omega}(\mathbf{x}_{\mathrm{noise}}),\mathbf{x})$$

Denoising Autoencoder Training Objective

The training objective for Masked Language Modeling (MLM) involves finding the optimal model parameters, $$\widehat{\mathbf{W}}$$ and $$\hat{\theta}$$, that minimize the total cross-entropy loss over a given dataset $$\mathcal{D}$$. For each modified text sequence $$\bar{\mathbf{x}}$$, the loss is computed only for the set of selected positions $$\mathcal{A}(\mathbf{x})$$ by comparing the model's predicted probability distribution $$\mathbf{p}_{i}^{\mathbf{W},\theta}$$ with the ground-truth distribution $$\mathbf{p}_{i}^{\mathrm{gold}}$$ at each selected position $$i$$. The complete optimization objective is formulated as:
$$ (\widehat{\mathbf{W}},\hat{\theta}) = \arg\min_{\mathbf{W},\theta} \sum_{\mathbf{x} \in \mathcal{D}} \sum_{i \in \mathcal{A}(\mathbf{x})} \mathrm{LogCrossEntropy}(\mathbf{p}_{i}^{\mathbf{W},\theta},\mathbf{p}_{i}^{\mathrm{gold}}) $$

MLM Training Objective using Cross-Entropy Loss

Consider a binary classification task where the correct label for a specific instance is `1`. A model makes two different predictions for this instance: Prediction A is `0.9` and Prediction B is `0.6`. According to the cross-entropy loss function, which statement accurately compares the loss for these two predictions?

A machine learning model is performing a binary classification task. For a single data point, the true label is `0`, and the model predicts a probability of `0.2` for the positive class (class `1`). Calculate the cross-entropy loss for this specific prediction. You may leave your answer in terms of the natural logarithm (ln).

Calculating Cross-Entropy Loss

A machine learning model is being trained to classify emails as 'Spam' (label=1) or 'Not Spam' (label=0). The model outputs a probability score indicating the likelihood of an email being spam. During one training step, the model makes the following predictions on four emails. Based on the principles of the cross-entropy loss function, which single email will contribute the most to the total loss for this step, and why?

Analyzing Model Errors with Cross-Entropy Loss

To train a language model, such as a decoder-only architecture, the standard approach is to minimize a loss function over a collection of token sequences. This function, denoted as $$\mathcal{L}(\mathbf{p}_{i+1}^{\theta}, \mathbf{p}_{i+1}^{\mathrm{gold}})$$, measures the discrepancy between the model's predicted probability distribution $$\mathbf{p}_{i+1}^{\theta}$$ and the true, gold-standard distribution $$\mathbf{p}_{i+1}^{\mathrm{gold}}$$ at each position. In natural language processing, this difference is typically quantified using the log-scale cross-entropy loss.

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