Difference between an Algorithm and a Model in Machine Learning

If you are at a crossroads, wondering which data science certification to take for a highly relevant career upgrade, opt for a Machine Learning course at a reputed institute. It teaches you all the essentials of big data analytics and steers your career path to a more focused data scientist job role as a machine learning engineer. 

Before registering for the course, you may like to clear some concepts about machine learning algorithms and models. So here we are, discussing what is an algorithm and a model? What is the difference between the two?

What is Machine Learning

IBM lays down the definition of machine learning: “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.” It provides systems with the ability to automatically and iteratively learn and improve from experience. Machine learning is also an important element of data science, where algorithms train to make classifications or predictions, to uncover insights from massive quantities of data.

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What is an Algorithm in Machine Learning

Some definitions of machine learning encapsulate the “algorithm” component of machine learning.

A McKinsey & Co. Insight states that “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.” A University of Washington paper mentions how “Machine learning algorithms figure out how to perform important tasks by generalizing from examples.”

So what is an “algorithm” in machine learning? A machine learning algorithm is a program that provides systems the ability to learn on their own and improve from experience without being explicitly programmed. Machines adjust themselves to perform better as they are exposed to more data. 

What is a Machine Learning Model

A machine learning model is a file that has been trained to recognize certain types of patterns. A set of data is used and provided with an algorithm to reason and learn from the data, and this is called training the model. Once the model is trained with the given set of training data, it can be used to reason with unseen data and make predictions about that data. For example, if you want to build an application that recognizes a user’s emotions based on facial expressions. The model can be trained by providing it with images of faces tagged with a certain emotion and then used in an application that can then recognize any user’s emotion. 

A machine learning model is thus a condensed representation of what a machine learning system has learned.  It is similar to a mathematical function that takes a request as input data, makes a prediction on that input data, and then serves a response. 

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The final set of trainable parameters of a model depends on the type of model. All machine learning models are categorized as either supervised or unsupervised. Where the model is a supervised model, it is further sub-categorized as either a regression or classification model. 

Difference between a Machine Learning Algorithm and a Machine Learning Model

An “algorithm” creates a machine learning “model.” A “model” is the output of a machine learning algorithm. The model represents what is learned by the algorithm. The Machine Learning model is “the “thing” that is saved after running an algorithm on training data and represents the rules, numbers, and any other algorithm-specific data structures required to make predictions.”

Examples

Examples of machine learning algorithms are linear regression, decision trees, convolutional neural networks, and reinforced learning. Some examples of machine learning models are Regression Models, Clustering Models, and Dimensionality Reduction Models.

Data set

Machine learning algorithms are executed in the code and run on data. Machine learning models are the output of the algorithms and consist of the model data and a prediction algorithm.

When you train an algorithm with known data, it becomes a model. 

Thus, Model = Training (of an Algorithm + Data)

Mathematical equation

Every algorithm has a mathematical underpinning, which, when executed in a machine, forms a machine learning algorithm. A model is an equation structured by discovering the parameters in the equation of the algorithm. The model specifies which family of functions the learning algorithm can choose from when varying the parameters to reduce the training objective. (Deep learning book, Goodfellow et al., 2016).

Algorithms are methods undertaken to get a task done or solve a problem, while Models are well-defined computations that are a product of an algorithm.

An algorithm takes some value as input and produces some value as output and is thus a sequence of steps with flow and loops, etc., transforming input to output.

Approach

An algorithm is an approach you take to solve a problem. The model is what you get when you run the algorithm on the training data and what you further use to make predictions on new data. A new model may be generated using the same algorithm but with different data, and a new model can be generated from the same data using a different algorithm.

Training data vs. new data

An algorithm is implemented in a programming language. An algorithm takes an input set of data and outputs an equation which is a model. Model is the output of the machine learning algorithm.

An algorithm can be used on different sets of training data. A model is then used as the deployment vehicle, which can take any unseen data in the future and produce an output prediction. That model has both data and a procedure for how to use the training data to make a prediction on new data, almost like a prediction algorithm.

Storing the entire dataset

Models are always triggered by the algorithm but not always dependent on the data. Based on the purpose that your model serves, some models like the k-nearest neighbors store the entire dataset, which acts as the prediction algorithm. 

Interrelationship

The algorithm behind the model matters most, as it is important to know which algorithm to apply to your model to yield the best predictions and right results. If the algorithm used is right, you will likely get a good model that works well on new data sets. However, a robust algorithm does not always yield a good model, as a machine learning model strategy depends on various factors other than the algorithm.

Ultimately, an algorithm is a few lines of code that you implement after much deliberation, while a perfectly working model is dependent on many other factors other than the algorithm or the training data.

Conclusion

Ultimately, a machine learning engineer works with big data to execute algorithms and build models for predictions. And a good way to kick-start a career in machine learning is to take a certification.

Kenneth

Kenneth is a proud native of sydney, born and raised there. However, he pursued his education abroad and studied in Australia. Kenneth has worked as a journalist for almost a decade, making valuable contributions to prominent publications such as Yahoo News and The Verge. Currently, he serves as a journalist for The Hear Up, where he focuses on covering climate and science news. You can reach Kenneth at [email protected].