MSE, Log Loss, Cross Entropy, RMSE, and the Foundational Principles of Popular Loss Functions
Welcome back! Within 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. On this post, we’d cover the next topics:
- What’s 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
- Tips on 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 function 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 concerning the extent of adjustments needed in its parameters, corresponding to weights or coefficients, to more accurately capture the underlying patterns in the info.
There are different loss functions in machine learning. These aspects include the character of the predictive task at hand, whether it’s regression or classification, the distribution of the goal variable, as illustrated by way of Focal Loss for handling imbalanced datasets, and the precise learning methodology of the algorithm, corresponding to the appliance of hinge loss in SVMs. Understanding and choosing the suitable loss function is kind of essential, because it directly influences how a model…