The supervised learning problems generally fall into three categories: binary classification, multiclass classification, and the last, regression problems.
With binary classification, there are only two possible outcomes, generally, yes or no. With multiclass classification, the result can have an infinity of possible categories, always more than two. Unlike binary and multiclass classification, regression problems tend to have a continuous solution. This last group of problems looks for trends instead of trying to classify the outcome into different groups.
Let’s explain better the differences between these three categories with some examples. The binary classification is used to solve problems where the answer can…
Artificial intelligence is part of our daily lives. Without realizing it, we are in constant iteration with different forms of artificial intelligence, which, in general, make our lives more comfortable. We can interact with them in different ways: on the internet, equipment in our house, on our phone, in the car, etc.
The quality of any predictive model is highly dependent on the choice of available data. The first step in any Machine Learning experiment is to collect the data related to the noticed situation that must be predicted. The relevant part of this data that can help us to learn how to solve our challenges is called a Feature.
In a few words, a Feature is an essential part of the observed information that is useful or meaningful to understand and learn how to solve a specific problem.
Having the best possible Machine Learning model can undoubtedly help achieve good results…