There are so many algorithms in machine learning that you need to learn. Understanding the use cases of every machine learning algorithm is very difficult for every data science beginner, but it is very important as it helps you to choose the most suitable algorithm for your problem. So if you want to learn about the use cases of different machine learning algorithms, then this article is for you. In this article, I will take you through the use cases of some of the popular machine learning algorithms.
Use Cases of Different Machine Learning Algorithms
It is very difficult for a data science beginner to identify the use cases of different machine learning algorithms. Some of the factors that can determine the use case of a machine learning algorithm are:
- Assumptions of the algorithm;
- Kind of data it requires;
- The type of output it gives;
- How much data is sufficient for it;
- Type of problem it is suitable for;
- Advantages and Disadvantages of the algorithm.
Many other factors can be used to determine the use cases for different machine learning algorithms. So let’s go through the use cases of some of the popular machine learning algorithms one by one.
The linear regression algorithm is one of the very first algorithms that you learn in machine learning. As the name suggests, it is used in problems of regression where the dataset you are dealing with is in a linear relationship. Some of the use cases of the linear regression algorithm are:
- Predicting the sales of a product
- Predicting demand or price of a product
- Predicting an increase in profit with the increase in promotions
The logistic regression algorithm extends linear regression with a logistic function that makes it suitable for classification-based problems. But you cannot use this algorithm for any kind of classification problem, you can only prefer it for problems based on binary classification. Although you can use it for multiclass classification problems, it is best only for binary classification problems. Thus, some of the use cases of the logistic regression algorithm are:
- Spam Detection
- Detection of credit risks
The Naive Bayes algorithm is based on the Bayes theorem and is a very popular classification algorithm. It can be used in both binary and multiclass classification problems. It has three variants; Gaussian (used when the dataset is normally distributed), Multinomial (used in multiclass classification problems), Bernoulli (used while working on binary classification problems). It is preferred when your classification task is based on natural language processing. In simple words, it is preferred when you want to train NLP models. Some of the popular use cases of this algorithm are:
- Classification of news categories
- Detection of fake news
- Detecting hate speech
Decision trees can be used for both classification and regression problems. But one of the advantages of using decision trees over other classification or regression algorithms is that this algorithm can handle non-linear datasets. If the dataset you are using has outliers and missing values, then it will not affect the performance of your machine learning model. So you can say that it is a very powerful algorithm and should be used when the dataset is not in a linear relationship.
So I hope you now have understood the use cases of some of the popular machine learning algorithms. I tried to keep the explanation simple and in plain English.
Understanding the use cases of every machine learning algorithm is very difficult for every data science beginner, but it is very important as it helps you to choose the most suitable algorithm for your problem. I hope you now have understood how to determine the use cases of different machine learning algorithms. In short, just go through the assumptions, advantages, and disadvantages of machine learning algorithms, and you will understand the use cases of machine learning algorithms.