Classification Models
This roadmap to AI is a blast!
Let us now take a look at Classification Models. “Classification in machine
learning is a predictive modeling process by which machine learning models
use classification algorithms to predict the correct label for input data. A
classification model is a type of machine learning model that sorts data points
into predefined groups called classes. Classifiers learn class characteristics
from input data, then learn to assign possible classes to new unseen data
according to those learned characteristics”. In the previous article, we mentioned Linear
Regression as part of an outcome of Supervised Learning Techniques. In this
type of Technique, you are labeling data to predict an outcome. In the case of
Linear Regression, that outcome will be a number. In a Classification Model,
however, the outcome will be a class. For example, you label the data based on
whether an image is a cat or not. You provide the model with the image (input),
and the output will be a class (Yes or No).
Another popular example is to
determine whether an email is spam or not. By labeling emails as spam or not,
the machine is trained into recognizing the type of email, by grouping them in
folders. The model will be trained until it is able to produce an output that
will classify with accuracy in which folder to place the email (spam or not).
This is called binary classification. But in a multiclass classification, the
model will be able not only to predict whether the image is a cat or not, but
also whether it is a cat, dog, bird, etc.
The are different types of
classification models, we will name just a few:
-
Logistic Regression: it reflects the relationship
between one or several input (independent variables) and an output (dependent
variables). In the case of classification models, the output will be a class.
Whereas in Linear Regression Regression the relationship was drawn on a
straight line, in a Logistic Regression (Classification) the line will simply “classify”
the values in groups (the line will swing and depending where is value is located
it will belong to one group of the other).
-
Decision Tree: in machine learning it is
a flowchart-like model used for classification or regression, where data is
split into branches based on feature conditions to reach a decision or
prediction.
-
Random Forest: it is an ensemble machine
learning method that builds multiple decision trees and combines their outputs
to improve accuracy and reduce overfitting in classification or regression
tasks.
-
Naïve Bayes: it is a supervised machine
learning algorithm based on Bayes' Theorem, used mainly for classification
tasks. It calculates the probability of each class given the input features and
selects the class with the highest probability.
These are just a few of many formulas
to classify label data. As you have probably noticed, Machine Learning is very
high on statistics. But the final goal is the same: to predict a class, be it
binary or multiclass outcomes. You are probably now beginning to grasp the role
of the Data Scientist in Machine Learning. They are experts that can build
Linear Regression or Classification Models, training the model to predict the
desired outcome. You can now see the infinite applications these models have in
real life: Will it rain or not? Will a potential customer default on his loan?
It a transaction fraud or legitimate? Based on a patients conditions, which disease
are they suffering from? These models have been around for decades, but the
rise of AI has made them more accurate enhancing their uses to sky limits! They
are already present in our daily life, and constitute the backbone of artificial
intelligence!
What is
Classification in Machine Learning? | IBM
What Is Logistic
Regression? | IBM
Getting
started with Classification - GeeksforGeeks
Classification
in machine learning: Types and methodologies
The
Ultimate Guide to Decision Trees for Machine Learning
10
Must-Know Models for ML Beginners: Random Forest | by Dagang Wei | Medium
Naive Bayes
Algorithm: A Simple Guide For Beginners [2025]
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