Exploring 11 popular machine learning algorithms

Because of its straightforwardness, linear regression stands out as a beginner-friendly machine learning algorithm. It establishes linear relationships between one variable and one or more other variables. For example, a real estate tool might want to track the relationship between house price (dependent variable) and square footage (independent variable). It’s considered “supervised” because you need to give it labeled data to train it to make these connections.

Its relative simplicity makes it very efficient when working with large data sets, and the output is easy to interpret and identifies insightful trends. However, this same simplicity is also why it struggles with complexities. Nonlinear patterns can confuse it, and it can easily be derailed by outliers. You also need to be careful to choose the right variables. Otherwise, the quality of the output can be seriously diminished.

Instead of focusing on connections, logistic regression algorithms are used to make binary decisions, such as “spam” or “not spam” for emails. It predicts the probability of an instance belonging to a particular class using various factors it is given. It can also provide insights into which factors influence the outcome the most.

Like linear regression, it handles large data sets well, but it also has some of the same flaws. It also assumes linear relationships, so complex, nonlinear patterns will cause it problems. If the data it's analyzing isn’t balanced, that can also create an imbalance in its predictions. For example, if most of the emails it’s looking at are “not spam,” then it might struggle to identify the “spam” emails.

Instead of making predictions, SVM algorithms find the widest margin between data classes. So, instead of predicting which emails are “spam” or “not spam,” it essentially draws a line to cleanly separate emails into those two categories.

Because they focus on the most important data and avoid getting tricked by irrelevant details, SVM algorithms are great in high-dimensional spaces. They also won’t be derailed by outliers and are energy efficient due to their focus on a subset of data points. But they’re also computationally expensive and training can be slow. They can also be difficult to interpret because of their complexity, and choosing the right parameters for the kernel function takes time and careful adjustment.

As the name suggests, decision trees follow a tree-like structure where it asks a series of yes or no questions. Think of it like a flowchart, where you keep making decisions until you get to the final answer. This final answer is your prediction. Decision trees are versatile supervised machine learning algorithms used to solve both classification and regression problems.

The best thing about a decision tree algorithm is that it’s easy to understand. You can easily follow the logic by looking at each decision it makes. It’s also very flexible, capable of handling different data types, and can continue making decisions despite missing data. Unfortunately, it’s also prone to overfitting and is very sensitive to the order and choice of features. It can also struggle with intricate relationships between variables, making it less accurate for complex problems.

The approximate nearest neighbor (ANN) algorithm and the k-nearest neighbor (kNN) algorithm are both related to similarity search and are used in machine learning for different purposes. kNN predicts a data point's category by finding the most similar points from the training data and mimicking the majority vote of their categories.

In simpler terms, both of these algorithms are designed to identify similar data points, such as similar products on an ecommerce site. They’re versatile algorithms that can handle various data types without too much pre-processing, and they excel at nearest neighbor search and anomaly detection. But they also both struggle as data gets spread across many dimensions, and it can be difficult to understand how they got to their decision.

Neural network algorithms — the basis for most modern AI tools — aim to mimic the structure of the human brain. They do this by employing layers of interconnected artificial “neurons” that learn through data processing to find patterns within the data. Neural networks are used for various tasks, such as pattern recognition, classification, regression, and clustering.

Neural networks are by far the most powerful and dominant ML algorithm today, capable of handling a diverse range of tasks from image recognition to natural language processing. They’re also extremely flexible and can automatically learn relevant features from raw data. They can do this continuously, and therefore, are adaptive to change. They’re also very data hungry, requiring vast amounts of data for training, which can be a problem if that data doesn’t exist. Because of the black box nature of neural networks, understanding how they reach their predictions can be very difficult.

A clustering algorithm is a type of unsupervised machine learning algorithm that groups similar data points together. The aim is to discover inherent structures in the data without requiring labeled outcomes. Think of it like sorting through pebbles by grouping them based on their similarities in color, texture, or shape. These algorithms can be used for various applications, including customer segmentation, anomaly detection, and pattern recognition. 

Because clustering is unsupervised, the algorithms don’t require labeled data. They’re great at pattern discovery and help with data compression by grouping similar data. The effectiveness is entirely dependent on how you define the similarities, though. And understanding the logic behind cluster algorithms can be challenging.

Anomaly detection (also known as outlier detection) is a process of identifying instances in a data set where the data significantly deviates from expected or “normal” behavior. These anomalies can take the form of outliers, novelties, or other irregularities. Anomaly algorithms are great for things like cybersecurity, finance, and fraud detection tasks.

They don’t need to be trained on labeled data, so they can even be unleashed on raw data where anomalies are rare or unknown. However, they’re also very sensitive to thresholds, so balancing false positives and negatives can be tricky. Their effectiveness also often relies on you understanding the underlying data and expected challenges. They can be extraordinarily powerful, but the more complex the algorithm, the harder it is to understand why something might have been flagged as an anomaly.

Random forests (or random decision forests) are ensemble learning methods used for classification, regression, and other tasks. They work by constructing a collection of decision trees during training. Random forests also remedy decision trees’ habit of overfitting to their training set.

By using a group of decision trees, random forests are able to produce much more accurate and robust results, and they can handle diverse data types. They’re relatively easy to interpret because you can analyze the decisions at the individual tree level, but for more complex decisions, understanding how it got there can be difficult. Because of the amount of computing power they need, random forests can also be expensive to run.

Gradient boosting is another powerful ensemble technique that combines multiple weak learners like decision trees in a sequential manner to iteratively improve prediction accuracy. It's like having a team of learners, each building on the mistakes of the previous one, ultimately leading to a stronger collective understanding.

By combining multiple trees (or other learning), gradient boosting can handle complex relationships with high accuracy and flexibility. They’re also very robust to outliers as they’re less susceptible to the influence of individual data points compared to other algorithms. Similar to random forests, though, they can be very expensive to run. It can also take time to find the optimal parameters that the algorithm requires to get the best results.

Q-learning is a model-free reinforcement learning algorithm used to learn the value of an action in a particular state. Think of it like an agent navigating a maze — learning through trial and error to find the quickest way to the middle. That's the essence of Q-learning, albeit in an aggressively simplified way.

The biggest benefit of Q-learning algorithms is that you don’t need a detailed model of the environment, making it very adaptable. It can also handle large state spaces, so it’s ideal for complex environments with many possible states and actions. This is great, but it’s not always easy to strike a balance between trying new actions (exploration) and maximizing known rewards (exploitation). It also has a high computational cost and rewards need to be carefully scaled to ensure effective learning.

Machine learning has quickly become a powerful tool driving innovation and efficiency across a wide range of industries. Enterprise solutions are increasingly using these algorithms to solve complex problems, streamline operations, and gain valuable insights from data. This isn’t surprising, considering the depth and variety you’ve seen from the 11 algorithms we covered in this blog.

At Elastic, we’re more than aware of the power and potential of machine learning. We’ve built a suite of solutions that hand businesses the power of machine learning out of the box. From real-time data analysis with Elasticsearch and Kibana to predicting potential issues in applications with Elastic APM, machine learning has become a key cog in our machine. And in security, we leverage anomaly detection to identify threats, while personalizing search experiences with algorithms like clustering.

Hopefully, you now understand how varied and important machine learning algorithms can be and maybe even got an idea or two about how you can use them yourself. The world of machine learning and AI will only grow and evolve over the coming years, so this is the perfect time to start getting involved!

Whenever you're ready, here are four ways we can help you harness insights from your business’ data:

1. Start a free trial and see how Elastic can help your business.

2. Tour our solutions to see how the Elasticsearch Platform works and how our solutions will fit your needs.

3. Discover 2024 technical trends: How search and generative AI technologies are evolving.

4. Share this article with someone you know who'd enjoy reading it. Share it with them via email, LinkedIn, Twitter, or Facebook.