Overfitting is an important concept all data professionals need to deal with sooner or later, especially if you are tasked with building models. A good understanding of this phenomenon will let you identify it and fix it, helping you create better models and solutions.
In brief, overfitting happens when your
As you might remember, supervised learning makes use of a training set to teach a model how to perform a task or predict a value (or values). It's also important to remember that this training data needs to be labeled with the expected result/right answer for every individual example
Selecting the right metric plays a huge role in evaluating the performance of a model. It can sound a bit exaggerated, but your project's chances of succeeding depend in great part on choosing a good performance measure. Making the wrong choice can result in models that are a poor fit
MAE and RMSE are some of the most common error metrics for regression problems.
Despite being used for the same task (understanding the errors in your predictions) there are important differences between the two. Choosing the right metrics for your model can make a huge difference in your ability to
Supervised learning is perhaps the most common form of machine learning task in use nowadays.
This form of learning makes use of a labeled training dataset to create a model that predicts a target of interest. If this line makes absolutely no sense to you, don't worry. After reading this