Training loss
- 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. It helps evaluate a model's performance during training, with the aim being to minimize this loss so that it can generalize well on unseen data.
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:
1. 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.
2. 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.
3. 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.
4. 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
The training loss is used to evaluate the performance of a machine learning model during training. To minimize this loss, optimization algorithms such as stochastic gradient descent (SGD) or Adam are employed. These optimization processes modify the model's weights in order to minimize its training loss.
Overfitting and Underfitting
Overfitting occurs when a model is too complex and fits the training data perfectly, yet fails to generalize well on unseen data. This leads to high training losses but low validation losses. Regularization techniques like L1 or L2 regularization can be used as penalties for complex models in order to minimize overfitting.
Underfitting occurs when a model is too simple and does not fit the training data well, leading to high training loss and validation loss. To avoid this issue, more complex models can be utilized or more data can be collected for training the model.
Explain Like I'm 5 (ELI5)
Training loss is like a test score for a machine learning model. The aim is to get the highest possible score by tweaking the model so it accurately predicts outcomes. Just like in school, if you get low marks on tests, you study harder in order to improve them; similarly, when a machine learning model experiences high training losses, efforts are made to reduce them and boost accuracy.
Explain Like I'm 5 (ELI5)
Imagine you have a large puzzle with many pieces. And you need to put them all together in order to complete the picture. However, sometimes you might make an error and place one piece in an incorrect location.
Machine learning is like that! The computer is trying to piece together a big puzzle, but instead of painting a picture, it's creating models that can predict outcomes. And just like humans do when solving a puzzle, the computer may make mistakes sometimes too!
The training loss is like a score that shows us how well the computer is doing at putting pieces of the puzzle together. A lower score indicates it is doing so correctly, while a higher one means it makes more mistakes.
When training a machine learning model, our goal is for its score (or loss) to be as low as possible so that it makes fewer errors and can provide us with accurate answers.