MSE, Log Loss, Cross Entropy, RMSE, and the Foundational Principles of Popular Loss Functions
Welcome back! In the ‘Courage to Learn ML’ series, where we conquer machine learning fears one challenge at a time. Today, we’re diving headfirst into the world of loss functions: the silent superheroes guiding our models to learn from mistakes. In this post, we’d cover the following topics:
- What is a loss function?
- Difference between loss functions and metrics
- Explaining MSE and MAE from two perspectives
- Three basic ideas when designing loss functions
- Using those three basic ideas to interpret MSE, log loss, and cross-entropy loss
- Connection between log loss and cross-entropy loss
- How to handle multiple loss functions (objectives) in practice
- Difference between MSE and RMSE
Loss functions are crucial in evaluating a model’s effectiveness during its learning process, akin to an exam or a set of criteria. They serve as indicators of how far the model’s predictions deviate from the true labels ( the ‘correct’ answers). Typically, loss functions assess performance by measuring the discrepancy between the predictions made by the model and the actual labels. This evaluation of the gap informs the model about the extent of adjustments needed in its parameters, such as weights or coefficients, to more accurately capture the underlying patterns in the data.
There are different loss functions in machine learning. These factors include the nature of the predictive task at hand, whether it’s regression or classification, the distribution of the target variable, as illustrated by the use of Focal Loss for handling imbalanced datasets, and the specific learning methodology of the algorithm, such as the application of hinge loss in SVMs. Understanding and selecting the appropriate loss function is quite important, since it directly influences how a model…
