Machine Learning Algorithm Classification | Guide for Beginners

Last updated by Rishabh Dev Choudhary on Sep 09, 2024 at 08:11 AM | Reading time: 14 minutes

Machine learning (ML) is one of the most exciting technologies that is used in data science. In fact, 48% of businesses globally use machine learning. You’ve probably heard this buzzword a lot of times but what does it actually mean? You may wonder: What is machine learning, how does it work, and what are the types of algorithms behind it. Today, we are going to learn all about machine learning classification and algorithm types. And don’t worry; we will do this in simple words. Even if you are a total beginner, you will understand everything.

What is Machine Learning?

Before we start with the algorithms, let’s understand what machine learning is. In layman terms, machine learning is all about making computers learn from data. In conventional programming, we tell the computer what to do with our code, while in machine learning, we feed data to computers and let computers decide what they need to do with that.

Imagine training a dog. You do not simply tell the dog what to do. Instead, you demonstrate to the dog what you want it to do, give it some treats, and keep on doing so until the dog masters that. Machine learning is like that – it's about teaching a computer to perform tasks without telling it explicitly how each of the tasks should be done.

Also read: What is Machine Learning? A Comprehensive Guide

The Basics of Machine Learning Classification and Algorithms

What does a computer do to learn? It uses algorithms! An Algorithm is a set of rules or instructions to solve a problem. In machine learning, they help computers understand the patterns of data and make predictions or decisions based on that data.

Before we go into the nitty-gritty of algorithms, it’s important to know that Machine Learning can be categorized in three main types:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Let’s break these down:

1. Supervised Learning

Supervised learning is like going to a school where the teacher gives you the answers and you learn by grasping the examples. In this type of learning, the algorithm is fed on a labeled dataset, which simply means that its input data is paired with its correct output. The algorithm learns how to connect inputs and outputs correctly, then it becomes capable of making predictions for new, unseen data.

Say you’re trying to teach a computer how to recognize pictures of cats. You’d feed it tons of labeled images — some that show cats and some that don’t — and the algorithm would figure out which ones have cats in them.

Example: Say you’re trying to teach a computer how to recognize pictures of cats. You’d feed it tons of labeled images — some that show cats and some that don’t — and the algorithm would figure out which ones have cats in them.
Common Algorithms:

Linear Regression: Predicts a continuous output (like predicting house prices).
Logistic Regression: Predicts a binary outcome (like spam vs. non-spam emails).
Support Vector Machines (SVM): Finds the best boundary that separates classes of data.
Decision Trees: Splits data into branches in order to make decisions (like a flowchart).
Random Forest: Group of decision trees works together to do a better machine learning classification and regression.
k-Nearest Neighbors (k-NN): Classifies data points on the basis of similarity to the examples in the training set.

2. Unsupervised Learning

Unsupervised learning is when you are given the data and left to discover patterns or a structure in the data on your own. The problem with this method, though, is that you only have input data; there’s no ‘desired output’ for specific inputs i.e. you don’t know what structure or pattern you are looking for. The algorithm has to work on its own to discover this.

Example: Imagine you had a bunch of photos but had no clue which ones were of cats; the algorithm would cluster together photos that looked similar and all the cat photos would be in one big group.
Common Algorithms:

k-Means Clustering: Groups data into clusters based on similarity.
Hierarchical Clustering: Does not only provide clusters of objects, but also arranges these clusters in the form of a tree which shows the relationships among them.
Principal Component Analysis (PCA): Dimensionality of data is reduced in order to make it easy to analyze while retaining the most important characteristics.
Autoencoders: Neural networks used to learn efficient data encodings.

3. Reinforcement Learning

When we talk about reinforcement learning, think of it as training a dog. So, whenever the dog does something bad, you hit it with a newspaper or something; when it does something good, you give it a reward. So there is this learning, the dog interacting with the environment and trying to figure out what's the best thing it can do to get rewards.

Example: Imagine a computer learning to play a video game. It will make a series of some good and some bad moves, but gradually modify the program so that it gets better and better at the game as measured by its score.
Common Algorithms:

Q-Learning: A value-based method where algorithms learn the value of actions in certain states.
Deep Q-Networks(DQN): These are the combination of Q-Learning with deep neural networks that make them strong in more complex environments.
Policy Gradients: It’s a method that directly optimizes the policy (strategy) that the agent follows, instead of directly trying to find the value of actions.

Also read: How to Become a Machine Learning Engineer in 2024?

Machine Learning Classification of Algorithms

When you get started with machine learning, the sheer number of different algorithms can be overwhelming. Each algorithm is like a tool that we can use to learn from data. It wouldn't make sense to use a hammer to solve every hardware problem you face, and instead, sometimes you'd use a screwdriver, and sometimes you'd try to figure out how to solve the problem differently, right? Similarly, it doesn't make sense to try every algorithm you hear about on any given problem. But how do you know which one to use? The answer is machine learning classification.

Machine Learning Classification Based on Learning Style

Firstly, we can categorize algorithms into the learning styles they adopt. Essentially, this refers to how they "learn" from the data provided. We discussed this earlier, but here's an insight into it.

1. Supervised Learning Algorithms

Supervised learning is that moment during a test when you look at your friend's paper for an answer. The algorithm is given a dataset in which every input also has its corresponding correct output (or label). Thus, the algorithm knows what it wants to achieve. It gradually learns how to map inputs to outputs (the way we learn how certain clues lead us to the right answer in a quiz).

They are your first option when you are able to specify what you need. Maybe you want to determine house price depending on a set of features like square footage, number of bedrooms, and location. Or maybe you just want to sort the email into spam and non-spam. In this scenario, you can already have quite a good idea of what the "correct" answer should look like, and you are just trying to learn your algorithm on how to find these patterns.

2. Unsupervised Learning Algorithms

Unsupervised learning is similar to being given a puzzle. There is no picture on the box. You try to figure out how all the pieces fit together without any other help. The algorithm doesn't receive any labeled data; it just tries to find hidden patterns (or hidden groupings) in the data by itself.

This kind of learning is super useful if you have new data and you're not sure what you want to find. For instance, Suppose you're a marketer and want to group your customers based on their purchase behaviors. Or suppose you have a dataset and want to find the weird entries in it (e.g., fraud detection!), unsupervised learning algorithms come to the rescue in these situations.

Also read: Artificial Intelligence vs Machine Learning: 9 Key Differences

Machine Learning Classification Based on Function

Now that we know about learning types, let's try to categorize algorithms in another way, i.e., by what they actually do. We can think of all the types of problems we can solve, and that would be our second categorization method.

1. Regression Algorithms

If the goal or output that you are trying to predict is in a continuous value – like, for example, predicting the price of a stock or the temperature tomorrow – then you will want to use regression algorithms. Those algorithms model how one variable (or a set of variables) might be impacting another one, and they are really helpful when we need to make very precise numerical predictions.

Common regression algorithms include:

Linear Regression: It is the classic algorithm that you look for when you want to predict a number based on some input features.
Ridge and Lasso Regression: Variations on linear regression that can help prevent overfitting (when your model starts getting too good at predicting in-sample and bad at predicting new unseen data).

2. Machine Learning Classification Algorithms

If you're working with categories or labels (e.g., deciding whether a tumor is cancerous or not or sorting pictures of animals into "dogs" and "cats") — you'll want to use machine learning classification algorithms because they let you assign items to discrete categories on the basis of their features.

Some popular machine learning classification algorithms are:

Logistic Regression: Though it is named so, this one is all about differentiating between the two classes of data, e.g., "spam" or "not spam."
Support Vector Machines (SVM): A powerful algorithm that tries to find the best boundary that separates your categories.
Naive Bayes: Naive Bayes is one of the simplest models based on probability that is very effective, even for text based machine learning classification tasks, which are considered as its best applications.

3. Clustering Algorithms

There are two types of machine learning classification clustering algorithms: unsupervised learning, where the task is to group similar items, and supervised learning, where it trains itself from labeled instances. They come in handy when you need a way of examining your data structures without already defined classes.
Examples include:

k-Means Clustering: One of the most simple and popular techniques for partitioning data into a specified number of clusters.

DBSCAN: This clustering algorithm is good at finding clusters of non-circular shape and size and can work with noisy data.

Gaussian Mixture Model (GMM): This is the more advanced technique of clustering by modeling data as a mixture of several Gaussian distributions.

4. Dimensionality Reduction Algorithms

It does not get better than dimensionality reduction algorithms when it comes to working with data that has too many features (hundreds or thousands). They help you simplify your data while losing as little important information as possible.
Some commonly used dimensionality reduction techniques are:

Principal Component Analysis (PCA): The go-to method for feature reduction on your data, so you have less to work with.

t-SNE: Great at being able to see "high dimensional" data by reducing to 2d or 3d.

Linear Discriminant Analysis (LDA): This one also does feature reduction, but what it does differently from PCA is it tries to find the best possible separability between classes (i.e. maximizing the separability).

Hence, this is particularly useful when you're performing machine learning classification tasks.

Choosing the Right Machine Learning Classification Algorithm

With so many algorithms, how would you know which one to choose? Here are a few tricks that will help you out:

Understand the data you have: Start by exploring your data. Is it labeled or unlabeled? How many features does it possess? Is it structured or unstructured? What you need your algorithm to do will be heavily influenced by the type of data you have.

Consider the Problem Type: Do you want to predict a numerical value (regression)? A class (machine learning classification), or discover hidden structures that you don't have any information about (clustering)? If you answer this question, you can eliminate some techniques already.

Don't go complex to start: It's really tempting to run towards the most complex algorithms you can find, but a simpler model such as Linear Regression or Decision Trees is actually a good benchmark against which to compare.

Experiment and Iterate: Machine learning is an art as much as a science. Try different algorithms, play with their parameters, and see how they perform. Most of the time, you get the best model by doing a lot of experiments.

Think About Interpretability: Sometimes you’ll need to be able to explain how your model makes predictions. Simpler models like linear regression or decision trees are easier to understand than, say, a neural network.

Ensemble: It's more than likely that one single algorithm is not the best fit for all machine-learning problems. In such cases, you can try ensembling to obtain better performance.

Parting Thoughts

Machine learning is an incredibly pervasive field, and basically any problem you want to tackle has a high chance of already having been studied or worked on by someone else. Whether you’re dreaming of predicting housing prices, classifying emails, segmenting customers, or optimizing game-playing strategies, there exists a machine learning classification algorithm that can get you started with your task at hand. The important thing is to remember that in machine learning, it’s all about data and problems first, simple working solutions next. Just keep experimenting. And when you start to get comfortable in a particular niche of machine learning, explore more algorithms specific to that niche. The more you know, the better the models get.

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FAQs: Machine Learning Classification

1. What is machine learning classification?

Machine learning classification is a type of algorithm used to categorize data into specific groups or classes based on its features. It's commonly used for tasks like spam detection, image recognition, and medical diagnosis.

2. How do I choose the right algorithm for machine learning classification?

Choosing the right machine learning classification algorithm depends on factors like the size and type of your dataset, the problem you're solving, and how important accuracy and interpretability are for your model. Experimentation is key!

3. What are some common algorithms used in machine learning classification?

Some popular machine learning classification algorithms include logistic regression, decision trees, support vector machines (SVM), and k-nearest neighbors (k-NN). Each has its strengths and is suited for different types of tasks.

4. Can machine learning classification be used for multi-class problems?

Yes, machine learning classification can handle multi-class problems, where data is sorted into more than two categories. Algorithms like decision trees, random forests, and support vector machines can be adapted for multi-class classification.

5. What's the difference between machine learning classification and clustering?

Machine learning classification involves sorting data into predefined categories based on labeled examples, while clustering is an unsupervised technique that groups similar data points without predefined labels. Both are used for different types of analysis.