A very common confusion among those new to machine learning is the difference between a machine learning algorithm and a Model in machine learning. The two terms are often used interchangeably, which makes it even more confusing. So in this article, I will tell you what is the difference between algorithm and model in machine learning.

## Is There a Difference Between Algorithm and Model?

My answer will be Yes, as a machine learning algorithm is like a procedure executed on data to find patterns and rules that are stored and used to create a machine learning model that is like a program that can be used to make predictions.

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## What is an Algorithm?

A machine learning algorithm is essentially a procedure that is used to find patterns within data and learn from the data. It is commonly said to be fit on a dataset which means it is applied on the dataset.

There are many types of algorithms with many different functions and purposes. The three main ones are:

- Regression: Used to make predictions where the output is a continuous value, such as logistic regression.
- Classification: are those algorithms that are used to classify between the categorical values.
- Clustering: Used to group similar items or clustered data points, such as K-Means.

## What is a Model?

A machine learning model represents the output of the algorithm. It represents what has been learned from “learning” the algorithm on the data and contains a specific set of functionality of the algorithm.

- A linear regression model stores the vector of coefficients and constants that best fit the data.
- A decision tree template stores the set of if-then statements corresponding to individual branches.

The model can be saved for later and acts as a program, using the previously stored functionality of the algorithm to make new predictions. If the model is trained efficiently and sufficiently, it can be used to make many more predictions on similar data with a certain level of precision and confidence.

## The Difference Between Algorithm and Model

Now as we know what an algorithm and a model in machine learning are, so it’s easier to see how they relate. As mentioned earlier, an algorithm is run on data to create a model.

This model includes both data and a procedure for using the data to predict new data. The process is just like an algorithm.

However, not all models store a prediction algorithm. Some, like the k nearest neighbours, store the dataset that serves as a prediction algorithm. This is all based on your model’s goal, however.

We want a machine learning model and don’t care as much about the algorithm behind it. However, it is important to know which algorithm to apply to your model for the best results. But once you know that, there are only a few lines of code and a few levels of interaction left before you have a perfectly working model.

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