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 {{see also|Machine learning terms}}
 ==Introduction==
-[[Training loss]] is an important [[metric]] in [[machine learning]] that measures the discrepancy between [[predicted]] [[output]] and actual output ([[label]]). It is the [[model]]'s [[loss]] during a specific [[training]] [[iteration]]. Training loss helps to evaluate a model's [[performance]] during training, with the aim being to minimize this loss so that it can generalize well on unseen data.
+In [[machine learning]], [[training loss]] refers to a [[metric]] of how well a [[model]] fits [[training data]]. It is the model's [[loss]] during a particular [[training]] [[iteration]]. The aim of [[training]] a [[machine learning model]] is to find [[parameters]] that produce the optimal fit with given information. To evaluate how well it does this, we use something called a [[loss function]] - also known as [[cost function]] or [[error function]].
+The training loss is the outcome of evaluating a loss function on [[training set]]. It measures the difference between predicted values and actual values ([[labels]]) for that set. The aim of training is to minimize this loss so that the model can make accurate predictions on new, unseen data with confidence.
 ==Types of Loss Functions==
-Machine learning employs a variety of loss functions, depending on the problem being solved and the model being employed. Some commonly employed loss functions include:
+Machine learning employs a variety of [[loss function]]s, depending on the problem being solved and the model being employed. Some commonly employed loss functions include:
-. Mean Squared Error (MSE) Loss: This loss function is the most commonly employed for regression problems. It measures the average squared difference between predicted output and actual output.
-. Binary Cross-Entropy Loss: Used in binary classification problems where the objective is to accurately predict one of two possible classes, this statistic measures the difference between predicted probability of a positive class and actual binary label.
-. Categorical Cross-Entropy Loss: Used in multiclass classification problems to predict one of several classes, this statistic measures the difference between a predicted probability distribution and an actual one-hot encoded class label.
-. Softmax Cross-Entropy Loss: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the categorical cross-entropy loss for each class and then takes its average across all classes.
+#Mean Squared Error (MSE) Loss: This loss function is the most commonly employed for regression problems. It measures the average squared difference between predicted output and actual output.
+#Binary Cross-Entropy Loss: Used in binary classification problems where the objective is to accurately predict one of two possible classes, this statistic measures the difference between predicted probability of a positive class and actual binary label.
+#Categorical Cross-Entropy Loss: Used in multiclass classification problems to predict one of several classes, this statistic measures the difference between a predicted probability distribution and an actual one-hot encoded class label.
+#Softmax Cross-Entropy Loss: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the categorical cross-entropy loss for each class and then takes its average across all classes.
 ==How Training Loss is Used==