As machine learning (ML) becomes more accessible and more businesses start to use it, there can be a lot of confusion around some of the common terminology used. Unfortunately, machine learning algorithms and machine learning models are sometimes (incorrectly) used interchangeably.
If you’re getting started with machine learning, understanding the difference between algorithms and models will help you better work with ML experts and make better use of machine learning data.
First, a short definition:
Machine learning algorithms are procedures that run on datasets to recognize patterns and rules.
Machine learning models are the output of the algorithm. Models act like a program that can be run on data to make predictions.
So, in the simplest terms, an algorithm is the procedure data scientists run on datasets to create a model which can then make predictions.
But it’s a bit more complicated than that. Let’s dive deeper into each of these terms.
What is a Machine Learning Algorithm?
The goal of a machine learning algorithm is to perform pattern recognition in order to learn from the data to create a machine learning model. There are several different types of ML algorithms based on the goal of the machine learning project, how data is fed into the algorithm, and how you want the algorithm to “learn.”
Supervised Learning: Data scientists provide the algorithm with known datasets and desired inputs and outputs and gradually correct the algorithm until it reaches a high level of accuracy.
Unsupervised Learning: The algorithm is run without correction from a human operator, allowing the algorithm to interpret and sort data without pr-established criteria.
Semi-supervised Learning: This algorithm uses both labeled and unlabeled data. The algorithm learns to label the unlabeled data.
Reinforcement Learning: The algorithm uses trial and error to reach an optimal result based on pre-established actions, parameters, and end values.
Read also: Understanding Self-Supervised Learning in ML
What is a Machine Learning Model?
When the machine learning algorithm learns from data using one of the approaches mentioned above, it creates a machine learning model. The model is the result of running an algorithm on data.
Once you have the model, you can use it to make new predictions on the data or on similar data sets. Depending on how effectively the algorithm was trained, the model will make predictions with a certain level of precision and confidence.
How to Use Machine Learning Algorithms and Models
So, what do algorithms and models mean in the context of data science? The goal of machine learning is to create predictions that you can use to make data-driven decisions for your business.
In order to do this, you need machine learning models that can produce predictions with a high level of confidence. It is very simple for an algorithm to produce a model with a 90% accuracy level. It can be very difficult to train this algorithm to increase the accuracy to 95% or higher. When making decisions based on the data ML models produce, a single percentage improvement in accuracy can make a huge difference. (In one instance, 1% point meant hundreds of millions of dollars on a supply chain).
Hopefully this helps you better understand the difference between machine learning models and algorithms. Use this information when working with machine learning experts to achieve better model accuracy and improve your company’s intelligence.