Classification Model : What it is and Algorithms Techniques You Can Choose.
Updated: Oct 27, 2022
Why the need of classification in machine learning?
Have you gone through a problem deciding which are the best fruits to choose for dinner tonight? This decision is broken down into deciding between an apple or pear. Slightly wrong in choosing what needs to be on the table for dinner that night, at that moment becomes a deciding factor to complement with other foods or you will kill the vibes of your palate. Some characteristics could be taste, colour and size. So these characteristics in machine learning are known as features.
There are a few reasons for why classification is needed in machine learning:
To be able to make predictions about new data instances
To be able to understand which features are most relevant for the classification task
To be able to understand how the classification algorithm works
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What is classification?
Machine learning models for classification are based on a mathematical function that can map input data (x) to discrete output labels (y). It can be generally stated as y=f(x). The goal is to learn a model that generalises well to unseen data. In other words, the model should accurately predict the output labels for new data points.
There are a few different types of classification models, including:
Support Vector Machines
Each model has its own strengths and weaknesses, and there is no single model that is best for all classification tasks. It is important to experiment with different models and tune the model’s parameters to find the best performing model for your specific classification task.
Let’s us see with examples :
In those two problems “Spam email” and “ Fraud detection” the variable that you want to predict can only be one of two possible values. No or yes. This type of classification problem where there are only two possible outputs is called binary classification.
A last example would be classifying categories of images. From the diagram, we can see that the model is trying to predict one out of the 3 classes (dog, cat and others). Machine Learning models are able to distinguish up to thousands of classes. This classification problem is called multi-label classification.
So how do we build classification algorithms?
There are several classification algorithms we can use such as logistic regression for probabilistic modelling, neural networks for non-linear classification, decision trees for learning a decision boundary, and Support Vector Machines (SVM) for best separation of the classes.
Logistic regression :
Logistic regression is a type of regression analysis that is used to predict the probability of a binary outcome that produces a logistic curve, which is limited to values between 0 and 1 (probability) and a binary outcome is a result that can only have two possible values, such as success or failure.
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Decision Trees :
Decision tree classification models work by partitioning the data into a series of distinct regions, known as nodes. Each node represents a decision point, and the tree is constructed by recursively partitioning the data until all of the data points are contained within a single node. The final node is then assigned a class label, and the tree can be used to predict the class label of new data points.
Decision tree is considered the most classification model that is easy to interpret.
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