Machine Learning Algorithm: How to Choose for ML Workflows in 2024

The variety of tasks that machine learning can help you with may be overwhelming. Despite this, the majority of tasks can be solved using a limited number of ML algorithms. Still, you need to know, which of them to choose, when to use them, what parameters to take into consideration, and how to test the ML algorithms. We’ve composed this guide to help you with this specific problem in a pragmatic and easy way.

What Is a Machine Learning Algorithm?

The variety of tasks that machine learning can help you with may be overwhelming. Despite this, the majority of tasks can be solved using a limited number of ML algorithms.

Still, you need to know, which of them to choose, when to use them, what parameters to take into consideration, and how to test the ML algorithms. We’ve composed this guide to help you with this specific problem in a pragmatic and easy way. Let’s start with the basics in case you’re still a bit in the dark about what this all is and why you might need it.

Machine learning is an algorithm-based method for analyzing data with the goal of looking for patterns and making accurate predictions. As the name suggests, ML algorithms are basically computers trained in different ways. These ways are the types of ML algorithms that fall into three and a half broad categories (we’ll explain the “and a half” part a bit later, be patient).

Humanity creates more and more data every day. It comes from a variety of sources: business data, personal social media activity, sensors of IoT, etc. Machine learning algorithms are used to take this data and turn it into something useful that can serve to automate processes, personalize experiences, and make complex predictions that human brains cannot do on their own.

Given the variety of tasks that ML algorithms solve, each type specializes in certain tasks, taking into consideration the features of the data that you have and the requirements of your project. Let’s take a look at each of the major types of ML algorithms and certain examples used for the most common tasks.

Types of Machine Learning Algorithms

There are three major types of ML algorithms: unsupervised, supervised, and reinforcement. An additional one (that we previously counted as “and a half”) is semi-supervised and comes from the combination of supervised and unsupervised. We’ll talk about the unique features and examples of each of these types.

Unsupervised ML Algorithms

This type of machine learning algorithm arguably represents artificial intelligence in its true form. Unsupervised ML is based on the idea that a machine can learn without any guidance from humans. For learning, it uses unlabeled data, which is basically raw data that can be found “in the wild” and is usually unstructured and unprocessed.

Naturally, unsupervised machine learning algorithms have a lot of limitations. As they don’t have any starting point for their training, there are only a few types of tasks that they can perform. The two major ones that we’ll highlight are clustering and dimensionality reduction.

Supervised ML Algorithms

This is arguably the largest and most popular group of machine learning algorithms. And no wonder: supervised learning is flexible, comprehensive, and covers a lot of the common ML tasks that are in high demand today.

In opposition to unsupervised learning, supervised algorithms require labeled data. This means that the models train based on the data that has been processed (cleaned, randomized, and structured) and annotated. The processing and annotation of the data is supervision that a human has over the training process (hence the name of supervised learning).

Data annotation is an essential process for building a supervised ML algorithm. In a nutshell, it requires adding labels or tags to the pieces of data, which will tell the algorithm how to make sense of it. It’s quite a time-consuming and labor-intensive process that usually gets outsourced to save time for the core business tasks.

There are quite a few interesting algorithm types in supervised learning. For the purposes of brevity, we’ll discuss regression, classification, and forecasting.

Semi-Supervised ML Algorithms

Supervised and unsupervised machine learning algorithms are very common for the majority of AI tasks today. Here’s a simple cheat sheet to facilitate your choice of a machine learning algorithm:

However, sometimes you cannot choose between either an unsupervised or a supervised ML algorithm. There are cases where combining the two algorithms can bring you more benefits even with regard to the growing complexity of your ML model. That’s because of the core features of each type of algorithm: unsupervised learning brings in simplicity and efficiency while supervised learning is all about flexibility and comprehensive goals.

When you combine two different types of algorithms, you get semi-supervised learning. This type of ML algorithm allows you to significantly cut down the financial, human, and time cost for annotating the data. At the same time, semi-supervised learning algorithms are not as restricted in the choice of tasks as supervised learning algorithms.

Reinforcement ML Algorithms

And now for something completely different. Unsupervised and supervised algorithms both work with the data, either unlabeled or labeled. A reinforcement algorithm trains within an environment with a set of rules and a defined goal.

Reinforcement learning algorithms are usually based on dynamic programming techniques. The idea behind this type of ML algorithm is balancing exploration and exploitation. There is some uncharted territory that an algorithm can explore but every action will be followed by a response from a system, either positive or negative. Training on these responses, the algorithm will learn to choose the best set of actions to achieve the set goal.

A classic reinforcement learning application is games such as chess or Go. Learning to play (and win) these games requires the algorithm to understand the environment (the board, the set of rules, and the actions that can be either punished (by the other player taking the pieces) or rewarded (by winning the opponent’s pieces). A more modern and fascinating example of a reinforcement algorithm is training autonomous vehicles. The algorithm is required to navigate the environment without hitting anything and obeying the traffic rules.

How to Choose Machine Learning Algorithm

Learning about the different types of machine learning algorithms is not enough to understand how to choose the one that fits your specific purpose. So let’s stick to an incremental method and see how exactly you can approach this problem.

Step 1. Understand Your Project Goal

As it has already become apparent, each machine learning algorithm was designed to solve a specific problem. So, first of all, you should consider the type of project that you’re dealing with.

To determine the right algorithm, start by asking whether the problem involves labeled data (supervised learning) or unlabeled data (unsupervised learning). If you have labeled data, further decide if the task is predicting continuous numeric values (regression) or discrete categories (classification). On the other hand, if the data is unlabeled, clustering techniques may be used to group similar data points. Additionally, consider whether dimensionality reduction is needed to simplify the data before applying other algorithms.

Answer this question: what kind of an output do you need? Do you need an algorithm for prediction based on previous data? Turn to supervised forecasting algorithms, such as regression for numeric predictions or classification for categorical outcomes. Are you looking for an image recognition model that will work with poor-quality photos? Dimensionality reduction in combination with classification will help you with it. Do you need to teach your model to play a new game? A reinforcement algorithm will be your best bet.

Step 2. Analyze Your Data by Size, Processing, and Annotation Required

When you’ve answered the question of what type of output you need, ask yourself what input do you have. What is your data like? Is it raw, just collected from wherever, and requires processing? Is it biased, dirty, and unstructured? Or do you already have a big annotated dataset on your hands? Do you have enough data or is additional collecting (or even collecting from scratch) required? Do you need to spend time preparing your data for the training process or are you good to go?

If your data lacks structure or labels, unsupervised learning techniques like clustering may be more suitable. Alternatively, if you have a well-prepared and annotated dataset, supervised learning approaches will help achieve more accurate predictions. Insufficient, poor-quality, unprocessed data usually doesn’t lend itself to great training of a supervised algorithm. You should decide if you want to spend time and resources on preparing the best data you can before starting the training process. If not, you can opt for unsupervised algorithms but keep in mind the limitations of such a choice.

Step 3. Evaluate the Speed and Training Time

Here’s another question for you to answer that can help you understand what type of machine learning algorithm you need. Do you need it fast even if it means lower quality of training (and, respectively, predictions)? More and higher-quality data lead to better training. Can you allocate the required time for proper training?

Step 4. Find Out the Linearity of Your Data

Another important question is what the environment of your problem is like? Linear algorithms (such as linear regression or support vector machines) are simpler and faster to train. However, they are not usually used for more complex problems as they deal with linear data. If the data is multifaceted, multidimensional, and has many intersecting correlations, linear algorithms might not be sufficient for your task.

Step 5. Decide on the Number of Features and Parameters

Finally, how complex and accurate your final AI model should be? Don’t forget that longer training usually leads to better, more accurate performance when the AI model is deployed. If you’re working with high-dimensional data, consider dimensionality reduction to simplify the problem and speed up the training process. You can specify more features and parameters for your model to interpret if you have time to let it train longer. So giving your algorithm more time to learn may be a good investment into your future output accuracy and interpretability.

Top Machine Learning Algorithms and Use Cases

For ML practitioners and data scientists, selecting the right algorithm is crucial for optimizing model performance and efficiency. This table offers a detailed overview of essential ML algorithms to enhance your ML workflows.

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FAQ

What are the 4 types of machine learning algorithms?

The four types of machine learning algorithms are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each type has specific use cases, depending on the availability of labeled data and the learning environment.

What is the most used ML algorithm?

The most commonly used machine learning algorithm is the decision tree, especially in the form of Random Forest and Gradient Boosting Machines, due to their versatility and effectiveness across various domains.

What is the easiest machine learning algorithm?

The easiest algorithm to understand and implement is the k-nearest neighbors (KNN) algorithm, which classifies data points based on the majority vote of their nearest neighbors in the feature space.

Is NLP a ML algorithm?

NLP (Natural Language Processing) is not a machine learning algorithm itself but a field that uses ML algorithms, particularly classification, clustering, and neural networks, to process and understand human language.

Written by

Karyna Naminas CEO of Label Your Data

Karyna is the CEO of Label Your Data, a company specializing in data labeling solutions for machine learning projects. With a strong background in machine learning, she frequently collaborates with editors to share her expertise through articles, whitepapers, and presentations.