# The Machine Learning Algorithms Everyone Should Know

March 31, 2022 - Emily Newton

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Artificial intelligence and machine learning are critical parts of today’s digital world. Everyone should be familiar with the main machine learning algorithms. These algorithms power today’s most important AI. Even for those not working in computer or data science, understanding AI is crucial.

AI and machine learning are central parts of modern daily life. Rising discussions about AI bias and the onset of Industry 4.0 have only increased this importance. These core machine learning algorithms are the methods behind the magic of many AI models.

**The Categories of Machine Learning Algorithms**

There are many types of machine learning algorithms, with new ones introduced every year. Algorithms fall into one of three main categories based on how they learn and make decisions. Most of today’s artificial intelligence models use a combination of ML algorithms.

**Supervised Learning**

This is the most common type of machine learning algorithm and the one that most new data scientists start with. When people think of machine learning, they are probably thinking of a supervised learning model.

In this category, developers teach algorithms using large sets of example data. Developers collect real-world data on whatever subject they want the algorithm to learn. Some of this data is for testing later on, but the algorithm processes most of it in the training stages. As the model sees more examples of data, it gets better at predicting how to categorize new data.

Within supervised learning, there are three sub-categories of algorithms. Classification algorithms use observations to categorize or classify items. Regression algorithms use observations to draw conclusions. These indicate the relationship between data points and variables. Forecasting algorithms make predictions based on patterns in past and present data.

**Unsupervised Learning**

Supervised learning uses structured, labeled data. In contrast, unsupervised learning algorithms work with completely unlabeled data. This is less organized than supervised learning and more hands-off for developers. The algorithm studies the data itself to draw conclusions, make predictions, and identify patterns.

**Semi-Supervised Learning**

This category of algorithms combines supervised and unsupervised learning. These machine learning algorithms study both labeled and unlabeled data sets. The labeled data acts as a guide to help the algorithm process the unlabeled data.

**Reinforcement Learning**

In this teaching method, an algorithm learns how to identify the best possible predictions through trial and error. Developers give the algorithm a set of rules about the data set and the actions it can take with each data point. Then the algorithm must assess all the possible results of each available action to find the best one. For example, this type of machine learning algorithm is common in AIs that know how to play games like chess or go.

**Linear Regression**

The first, and most popular, type of machine learning algorithm is linear regression. This is the go-to starting point for those who are new to machine learning and remains a great algorithm for a wide range of models. Linear regression is a statistical regression supervised learning algorithm.

Linear regression algorithms take a scatter plot of data containing two variables, input and output. The goal is to find the most accurate linear function to represent the relationship between these variables. The result is known as predictive modeling.

By analyzing input data, the algorithm learns to identify patterns within the data. These patterns show which data points correlate to which variables. The algorithm uses a system of mathematical weights and biases to do this. Weights identify which characteristics are more relevant in determining how to categorize data.

**Logistic Regression**

Like linear regression, logistic regression identifies the function that most accurately separates data points. Logistic regression uses an S-shaped logistic curve to plot this relationship, rather than a linear function. This statistical classification supervised learning algorithm predicts probability.

For example, a logistic regression algorithm could predict whether an email is spam. This model is good for “either-or” tasks with a binary set of categories to sort data into. While logistic regression might seem simple on the surface, it is highly efficient to train and easy to expand for complex data sets. This makes it a highly versatile machine learning algorithm.

When there are multiple categories involved, the logistic regression algorithm becomes “multinomial”. On a basic level, a multinomial logistic regression algorithm is 3D rather than 2D. This may be challenging for us to visualize, but for an AI this is simple.

Developers can add even more precision by adjusting the algorithm’s “decision threshold”. Logistic regression algorithms can have either low precision and high recall or high precision and low recall. A low precision/high recall decision threshold will have fewer false negatives. This means there may be more results that are a “false alarm”. This is the decision threshold that is most often used in medical machine learning. A high precision/low recall decision threshold has fewer false positives. This is helpful for applications like filtering spam emails.

**Decision Trees**

A decision tree is a flow chart that maps out many possible predictions for a set of data. The algorithm filters data through “nodes” to create pure nodes that cannot be split up further, ending in “leaves”. The lines connecting all of the nodes and leaves are “branches”. These represent relationships between nodes and their clusters of data.

Decision trees can be either classification or regression algorithms. It depends on the algorithm’s goal. In fact, they are also known as “Classification And Regression Trees” or CARTs. By splitting up data into the tree’s nodes, the machine learning algorithm can precisely predict what will happen to new input data.

For example, a decision tree algorithm could predict if a passenger would survive the Titanic disaster. It could do this by testing the passenger’s variables. The algorithm would use variable characteristics like age, gender, and swimming ability. The algorithm compares these variables to the existing data it has on Titanic survivors.

Decision trees are highly effective. They do need a bit more attention than some other machine learning algorithms, though. A common issue with decision trees is “overfitting”. This occurs when a tree continues growing beyond a point where it is actually effective.

A common example is a tree that splits data into leaves so specific they contain only one or two data points each. Developers may need to set a minimum number of input data points required for each leaf. This will keep the algorithm generalized enough to be accurate without being overly complex.

**Random Forests**

Connected to the decision tree algorithm is the random forest algorithm. A random forest machine learning algorithm combines multiple decision trees to make predictions. As a result, this ensemble of disconnected trees forms more accurate results as a whole. Random forest machine learning algorithms use a function called “feature bagging”. This allows the algorithm to hone in on a specific subset of splits in the decision trees. This keeps the algorithm as a whole more focused than a single decision tree might be.

In a random forest system, each of the decision trees gets its own unique chunk of the training data to learn from. How each tree’s results are handled depends on the algorithm’s goal. If the algorithm is being used for a classification task, the results of the forest will be the “majority vote” of all the trees’ results. If the algorithm is being used for a regression task, the results will be an average of the trees’ results.

While the random forest algorithm is more complex, it does offer some key advantages. A random forest will not suffer from overfitting. Its scale also allows for plenty of flexibility. The complexity of random forest algorithms requires more storage and processing power, though.

**K-Nearest-Neighbors**

The K-Nearest-Neighbors, or KNN, machine learning algorithm is distinct from other types. This supervised learning algorithm draws conclusions by finding a certain number, “K”, of similar data points.

K could be the average or most common value. Which one depends on whether the algorithm is for regression or classification. The KNN approach assumes that data points that are nearby one another must have characteristics in common. KNN algorithms often use Euclidean distance to pinpoint nearby data instances. This is a specific distance between points that can be calculated right in the algorithm’s code. The Euclidean distance method only works with data points that are all on the same scale, though.

KNN machine learning algorithms look at the entire data set at once. So, they tend to be memory-intensive. They are easier to pivot, though. Since these algorithms only perform calculations when a prediction is requested, it is easy to tweak and update the data set.

**Exploring Machine Learning Algorithms**

Without machine learning algorithms, AIs would not be nearly as advanced as they are today. Machine learning creates the difference between true artificial intelligence and a preprogrammed bot. With machine learning, an AI can adapt and grow beyond the bones of its initial code. Exploring machine learning algorithms helps us to develop AI models that can evolve into experts at anything.

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