What Are Machine Learning Models and How Do You Build Them?

Key takeaways:

• Machine learning models: Programs that recognize patterns in data to make predictions.

• Building models: Train algorithms with labeled, unlabeled, or mixed data.

• Popular algorithms: Classification (e.g., logistic regression, decision trees) and regression (e.g., linear regression, neural networks).

Machine learning models are critical for everything from data science to marketing, finance, retail, and more. Today, the machine learning revolution has changed not only how businesses operate but entire industries as well.

But what are machine learning models? And how are they built?

To build an understanding of machine learning models, explore what they are, how to create them, and what types of popular algorithms act as their foundation. To start learning, look at suggested courses and articles that can help guide you toward machine learning mastery. 

What is a machine learning model?

Machine learning models are computer programs that are used to recognize patterns in data or make predictions.

You create machine learning models by using machine learning algorithms, which undergo a training process using either labeled, unlabeled, or mixed data. Different machine learning algorithms suit different goals, such as classification or prediction modeling, so data scientists use different algorithms as the basis for different models. As you introduce data to a specific algorithm, it is modified to better manage a specific task and becomes a machine learning model.

For example, a decision tree is a common algorithm used for both classification and prediction modeling. A data scientist looking to create a machine learning model that identifies different animal species might train a decision tree algorithm with various animal images. Over time, the algorithm would become modified by the data and increasingly better at classifying animal images. In turn, this would eventually become a machine learning model. 

Machine learning types: models vs. algorithms

Despite their differences, many people use these two terms interchangeably. Machine learning algorithms are programming procedures. They are methods created to solve a problem or complete a task. Machine learning models are the output of these procedures, containing the data and the procedural guidelines for using that data to predict new data.

How is a machine learning model built?

You can create machine learning models by training algorithms with either labeled data, unlabeled data, or a mix of both. Four primary machine learning algorithms exist:

1. Supervised learning

• Training: Uses labeled data which is data tagged with a label so an algorithm can successfully learn from it. 

• Purpose: Helps the algorithm classify data as desired.

2. Unsupervised learning

• Training: Uses unlabeled data. 

• Purpose: Algorithm finds patterns and creates data clusters.

3. Semi-supervised learning

• Training: Combines labeled and unlabeled data.

• Purpose:  Algorithms are trained first with a small amount of labeled data, then with more unlabeled data.

4. Reinforcement learning

• Training: Assigns positive and negative values to desired and undesired actions.

Purpose: Encourages programs to maximize rewards through trial and error and avoid the negative training examples.

What are parameters in machine learning models?

Before machine learning engineers train a machine learning algorithm, they must first set the hyperparameters for the algorithm, which act as external guides that inform the decision process and direct how the algorithm will learn. For instance, the number of branches on a regression tree, the learning rate, and the number of clusters in a clustering algorithm are all examples of hyperparameters. 

As the algorithm is trained and directed by the hyperparameters, parameters form in response to the training data. These parameters include the weights and biases formed by the algorithm during training. The final parameters for a machine learning model are the model parameters, which ideally fit a data set without going over or under. 

While you can identify a machine learning model’s parameters, you can’t identify the hyperparameters used to create it.

Types of machine learning models

Two types of problems dominate machine learning: classification and prediction. 

You can approach these problems using models derived from algorithms designed for either classification or regression (a method used for predictive modeling). The same algorithm can occasionally be useful for classification or regression models, depending on its training. 

Explore the following list of popular algorithms for classification and regression models. 

Machine learning classification models

• Logistic regression 

• Naive Bayes 

• Decision trees 

• Random forest 

• K-nearest neighbor (KNN)

• Support vector machine

Machine learning regression models 

• Linear regression

• Ridge regression 

• Decision trees

• Random forest 

• K-nearest neighbor (KNN)

• Neural network regression

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