In practical terms, AI is a set of algorithms that allow engineers to solve problems with a high degree of complexity. The reason to use AI is because it allows the solution to very complex, though specific problems.
AI offers the solution to a variety of different problems: for each defined category of problems we use what we call a paradigm. AI offers a solution to three defined paradigms: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Given their complexity, today I will focus on describing which are all the main Supervised Learning tools provided by the AI to solve this category of problems.
Supervised learning works when the data can be divided into Features (the data that we use as a predictor) and Labels (the part of the data we wish to predict). We start with a dataset that has both, and after the AI figures out the rules that binds them together, it can make an estimation on a new dataset.
However, there are several different configurations of our problem. Each one of these configurations has a specialized AI to solve that problem.
Let us begin by a clear representation of our dataset:
First of all, let us look at all the possible configurations to structure our Labels:
- Single Label
A single column of labels (survivors of the Titanic)
Multiple columns of labels (students IQ and grades)
The content of the labels for Classification:
- Binary Classification
Labels can only have 2 values (cat or dog)
- Multi-Class Classification
Labels have 3 or more values (cat, god, fox…)
And the content of the labels for Regression:
Multiple inputs, single output
- Multi-Output Regression
Multiple inputs, multiple outputs
Combined, we obtained the following scheme: it summarizes all the possible ways we can classify a problem we wish to solve using AI (using Machine Learning algorithms or Multilayer Perceptron neural networks).
- Iris classification
The dataset requires a classification approach, it has one single label, 4 kind of iris:
Classification, Single-Label, Multi-Class Classification
- Titanic survivor prediction
The dataset requires a classification approach, it has one single label (if a person survives or not), 2 kinds of choices (alive or dead):
Classification, Single-Label, Binary Classification
- Predict student grades with their IQ
The dataset requires a regression approach, it has two labels (grade of students and IQ), we require one output for both predictions:
Regression, Multi-Label, Regression