In a previous article, we learned about supervised learning: using labeled examples for creating models that solve a variety of tasks such as classification and regression.
There is another type of learning that doesn't require labeled examples: unsupervised learning. As it turns out, unlabeled data is much more common than
Categorical data is extremely common in most real-world machine learning applications. The main problem is that most algorithms don't really know how to manage categorical data: they are really good at working with numbers but don't really understand the concept of category. Because of this, it's important to have a
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