Understanding the Types of Machine Learning Algorithms

Understanding machine learning algorithms

Algorithms are crucial in machine learning. AI couldn’t learn or become more efficient, accurate, or intelligent without them. However, selecting the right type of machine learning algorithm can be challenging because there are several different options.

The right machine learning algorithm for you will depend on several factors, such as your goals, data available, training time available, and the complexity of the AI model.

Ultimately, you must determine what kind of output is required since each algorithm is designed for a specific type of project.

Types of machine learning algorithms

Depending on your desired outputs and results, you can use and experiment with several types and subtypes of machine learning algorithms.

The four machine learning algorithm learning styles are:

Supervised learning algorithms

Supervised machine learning algorithms learn by reviewing existing training data.

For example, with these machine learning algorithms, you’ll give the machine a dataset with desired input variables and outputs, ultimately giving it the answer to the question, which it can relay back to you.

In other words, the machine is trained using labeled data and already knows the correct answers.

These machine learning algorithms are directly trained on data, teaching models that provide accurate results because machines already have the desired result. Over time, the supervised machine learning algorithm learns what you want it to do based on your provided data.

There are two subsets of supervised machine learning:

Classification

Classification machine learning algorithms are used when the program must provide a result or conclusion from existing values.

For example, your email program uses a classification algorithm to learn which types of emails should be categorized as spam. You can also create triggers telling the program which types of emails are spam by telling them what to look for.

Regression

With regression machine learning algorithms, the program understands various variables or data points involved, such as when the result is a real value subject to change. Regression machine learning algorithms are most commonly used for prediction and forecasting.

This machine learning algorithm model, for example, can be used for financial projections to determine how much your business will generate in revenue if a variable like sales changes.

Many businesses already use supervised learning to improve decision-making, learn more about customer behavior, and save time sifting through data.

Since supervised learning is primarily based on rules, it will only match data under the correct conditions, allowing you to segment customer interactions based on the type of interaction or various factors, like age, gender, and location, for improved marketing performance.

Additionally, it can help you analyze and predict customer behavior, allowing you to improve the customer experience based on real data from your customers.

Unsupervised learning algorithms

Supervised learning relies on fully labeled data the machine can learn from. Unfortunately, this data isn’t always available, in which case a business might use unsupervised machine learning algorithms.

Furthermore, unsupervised machine learning models don’t give the machine the correct answers. Instead, the dataset comes without proper instructions, and the machine must find a way to use the available data by analyzing it.

Unlike supervised learning, unsupervised learning can help reduce human bias because there’s less human intervention required. Since the data used is unlabeled, it can help avoid some errors in the data itself. However, humans still interpret the data, so there’s still room for bias overall.

Unsupervised learning organizes data in several ways, including clustering, anomaly detection, and association. Since it hasn’t been trained with the correct answers and only has unlabeled datasets, it must discover patterns on its own and draw conclusions based only on the data it has.

Unsupervised learning can draw conclusions based on similarities and differences in the data to provide businesses with crucial insights.

These machine learning algorithms are also commonly used in business to segment customers. For example, it can group customers together based on similar features like age, gender, interests, location, and so forth.

Unsupervised learning can also be used to visualize information by helping you organize all the data your business collects into patterns that might take a human data expert many weeks to interpret.

Semi-supervised learning algorithms

Semi-supervised machine learning algorithms are a combination of supervised and unsupervised learning, which uses a mix of labeled and unlabeled data to draw conclusions.

With these models, machines may have labeled and unlabeled data sets that allow them to draw conclusions based on results already given to them, and those they’ll have to draw on their own based on association and clustering from the unsupervised model.

For example, a machine might have labeled data and images of different animals and have to label any unlabeled data based on the various similarities between animals. It might have some images labeled as “dog” or “cat” and some not.

In this case, it would look at similarities between the animals to determine which unlabeled datasets are dogs versus cats.

Reinforcement learning algorithms

Reinforcement learning algorithms teach machines to complete multi-step processes with clearly defined rules and parameters.

With reinforcement learning algorithms, the machine decides the steps it takes to complete a process while you give it guidance (reinforcements) along the way by defining the rules.

Then, the machine algorithm explores different methods for completing processes via trial and error, eventually learning from its mistakes to create a completely new approach.

Reinforcement learning depends on positive and negative reinforcement. Positive reinforcement learning is a type of event that occurs because of a trigger or behavior, increasing the frequency of the behavior to maximize performance.

Conversely, negative reinforcement strengthens the machine’s behavior by giving it negative conditions to prevent it from making the wrong decision throughout the process.

In robotics, reinforcement learning is typically used to teach robots to perform various tasks. However, it can also help enterprises learn new processes for allocating resources and building new processes for a variety of tasks.

Factors to consider when choosing machine learning algorithms

Choosing the right machine learning algorithms for your particular use depends on several factors, including data quality and amount, your goals, and the complexity of your AI model.

You should also consider your particular needs and the time you have available to train the algorithm. Let’s look at a few factors that may impact your decision:

Type of problem being solved

Before you begin developing your machine learning algorithm, you must consider your goal — the problem being solved. For example, do you want your algorithm to project financial information to improve business intelligence efforts, or will you use it to improve customer service through chatbots?

Every algorithm available is designed to solve a problem, so you should always consider the type of output or result you need.

For example, are you forecasting or trying to organize data? If so, you might choose either an unsupervised learning algorithm or a supervised model.

Amount and quality of data available

The amount of training data available is another crucial factor because more data means better, more accurate results.

Supervised algorithms rely on quality, accurately labeled data sets. If you don’t have labeled data, you might be better off with an unsupervised model that organizes your data for you.

Complexity of the data

Complex data means time-consuming machine learning algorithms. Therefore, gathering as much data as possible is recommended to improve the algorithm’s accuracy.

Whether your data is labeled, unlabeled, or a combination of both will determine which algorithm is right for you. For example, if all your data is labeled, you might benefit from a straightforward supervised learning model. If you have unlabeled and labeled data, you’ll have to choose either an unsupervised or semi-supervised model.

More data complexity typically leads to more accurate results. However, it’s much more expensive and can be harder to explain the results.

Time and resources available

Some machine learning algorithms require more time and resources than others due to the complexity of the data. All machine learning models take time, but some require more human intervention than others because they must use training data to teach the algorithm.

Accuracy and interpretability of the model

Unfortunately, even the best data scientists can’t always determine which machine learning algorithm is right for a particular project. Testing different models can help you determine whether one is more accurate and interpretable than the other.

Supervised models are typically more accurate than unsupervised models because they have extensive labeled training data to learn from.

Popular machine learning algorithms and their applications

We’ve already discussed the different types of machine learning models available to choose from. However, different algorithms can be grouped together by how they work and which category of algorithm they fall under.

Here are a few different examples of the particular types of machine learning algorithms you can use and their applications:

Linear regression

Linear regression is a type of supervised regression-based learning algorithm that helps humans understand the relationship between variables.

This algorithm performs tasks predicting a dependent variable based on provided independent variables to determine a linear relationship between the dependent variable and the independent variable(s).

An example of linear regression would be comparing sales data and monthly sales to forecast future sales.

Logistic regression

Another type of supervised learning algorithm that determines the relationship between two or more variables is based on logic. It’s often used to categorize or classify variables and predict or forecast the dependent variable outcome.

For example, you can use logistic regression to help an email program predict whether an email should be labeled as spam or not spam.

Decision trees

Decision trees are another supervised machine learning algorithm used to classify or categorize data. This type of algorithm is more visual than others, allowing you to see how each category has been classified and by which variable.

Decision trees are used for numerical and categorical datasets and can capture non-linear relationships between variables. For instance, you could use decision trees to segment customers based on a variety of factors like age and location.

Random forests

Random forests is another supervised machine learning algorithm used for classification and regression. The dataset is passed through decision trees, and the algorithm constructs variations of these trees and outputs the class or classes of the data. For regression, it provides a prediction.

Random forests are best at learning non-linear relationships between variables, and the outputs are easy to interpret. You could use decision trees to help you determine every possible outcome of various tests.

For instance, you can determine what happens to your revenue if sales decrease during a particular month or categorize customer interactions to determine which customers are most likely to purchase your products.

Support vector machine algorithm

The support vector machine algorithm (SVM) is another supervised learning algorithm used for classification by segregation or categorizing datasets by class.

Support vector machines will filter data into several categories based on the training data provided. These models can segment customers, predict consumer behavior, and provide detailed analytics to fuel your marketing campaigns.

Naive Bayes

Naive Bayes is a supervised machine learning algorithm used for classification and prediction. This algorithm applies the Bayes theorem with independent assumptions, referred to as “naive” because it assumes that the variables are independent.

This method can be used for marketing analytics to help marketers understand various customer segments and data.

K-nearest neighbors (KNN)

The KNN algorithm is another unsupervised machine learning model that estimates the likelihood of data being related to another group of data. Instead of grouping data together, it classifies data based on its nearest “neighbors.” KNN is typically used for data mining and financial predictions by looking for similar data.

Clustering algorithms

Clustering algorithms are part of an unsupervised machine learning model and are used to categorize and group unlabeled data sets without predefined categories. This method works by grouping — or clustering — data by finding similarities between them and assigning each data to a group based on its features.

A clustering algorithm can be used for customer segmentation by assigning customers to a particular cluster based on several attributes like age, location, gender, interest, and any other information you’ve collected about them.

Then, once your customers are clustered, you can create a marketing approach for each distinct group to provide more personalization with each campaign.

Association algorithms

Association algorithms are rule-based methods for determining relationships between several variables and are typically used for understanding customer purchasing habits.

With this model, the machine analyzes large data sets involving purchase history, including products purchased, to find groupings associated with one another. For example, a person who purchases dog food and toys is likely to purchase dog treats.

This type of algorithm can be used to give customers product recommendations in e-blasts or directly on website pages based on actions they’ve already taken on your website, such as viewing or purchasing specific products.

Choosing the right algorithm for your business needs

Choosing the right algorithm for your business requires understanding your ultimate goals and the desired outcome. However, you should also consider the quality and quantity of your training data.

For example, some algorithms are determined based on whether you have labeled or unlabeled data. However, the more labeled data you have, your machine learning algorithms will become more accurate.

In some cases, you might need to experiment with different types of machine learning algorithms to determine which is best for your particular use.

For example, we’ve mentioned several that can help segment customers, but what you decide on depends on your available training data and how much time you want to invest in the algorithm.

The various types of machine learning algorithms are designed to improve accuracy, efficiency, and decision-making within your business. However, regardless of the type of machine learning you use, it still requires some level of human intervention to interpret the data.

With Mailchimp, you can leverage machine learning through automation to segment your customers, learn from customer data, and create more effective marketing campaigns with machine learning algorithms that allow you to personalize your message.