Types of Machine Learning Algorithms You Must Know
With machine learning (ML) applications can predict outcomes more accurately. The algorithm of machine learning utilizes the history data to predict new outcomes. Machine learning and AI are buzzing words in recent days. Basic knowledge about the types of machine learning algorithms will help to select a suitable model for your project.
Types of Machine Learning Algorithms
Depending on the data the scientist chooses a machine learning algorithm. There are mainly four basic types of machine learning algorithms. And here is the list of machine language algorithms
- Supervised learning
- Unsupervised learning
Now, we will discuss these classifications of machine learning algorithms briefly.
As part of supervised learning, there is an input variable (x) and an output variable (Y). By learning the mapping function, you can translate the input into the output.
Y = f(X)
The main goal of supervised learning is to approximate the mapping function. So that when you new input data (x) that you can predict the output variables (Y) for that data.
Linear Regression is one of the best types of machine learning algorithms arguably. Correlation between two variables is mapped through linear regression algorithms. To show the relationship in a set of inputs and their corresponding outputs are examined and quantified. On a graph, linear regression is plotted as a line.
Linear regression is a simple algorithm that’s why it is popular among people. . This Algorithm is easy to explain, transparent, and requires little t no parameter tuning. In enterprises seeking to make long-term business decisions, linear regression is often used in sales forecasting and risk assessment.
Linear Regression works for a numerical variable. On the other hand, the logistic regression algorithm builds a relationship between variables and a class. This algorithm can predict future events if there are certain types of events known to us in advance. The dependent variable is indeed a categorical variable but the inner working of the Logistic regression algorithm transforms the variable. In other words, it calculates the log odds ratio and builds a linear equation from it.
Support Vector Machines
In the field of supervised learning, SVM (support vector machine) is one of the most popular algorithms. This algorithm is used for classification problems as well as Regression problems.
Using SVM algorithms, n-dimensional space can be divided into classes using a decision boundary. In the future, this can make it easier for us to place the new data point in the right category. In this algorithm, the hyperplane is called the perfect decision. Using the support vector machine, the hyperplane is formed by the extreme points or vectors.
Decision trees machine language algorithms are non-parametric supervised learning techniques. Both classifying and regressing problems can be solved by using them. To create a tree-like structure, it needs to determine appropriate methods for splitting data up based on various conditions. With these conditions, you can forecast an event or value.
Semi-supervised learning teaches algorithms through both labeled and. This Algorithm learns specific information through a set of labeled categories, suggestions, and examples. After that, semi-supervised algorithms then create their labels by exploring the data.
Generative Adversarial Networks (GAN)
The GAN model is a deep generative model that has gained popularity. Generative adversaries can mimic data to model and predict. A model is essentially pitted against another in a competition to find the best solution to a problem.
Self-trained Naive Bayes classifier
Semi-supervised learning is exemplified by self-trained algorithms. Developers can add a Naive Bayes classifier to these models. Which allows self-trained algorithms to execute classification tasks quickly and easily.
Unsupervised Machine Learning Algorithms
Data scientists do not train unsupervised machine learning algorithms. Deep learning uses unlabeled training data sets in conjunction with correlations to identify patterns in data. Unsupervised learning models do not know which data features to examine or what to look for in the data.
In market basket analysis, Apriori algorithms are typically used to mine item sets and create association rules. When two variables in a data set are correlated, the algorithms check for a positive or negative correlation.
K-means is an iterative algorithm for sorting data into groups based on similar characteristics. clustering machine learning algorithms like K-means sort web results for the word civic into groups relating to Honda Civic and civic as in municipal or civil. K-means clustering is reputed to produce accurate, streamlined groupings in a relatively short period.
The algorithm which is based on a system of rewards and punishments learned through trial and error is called reinforcement learning. Reinforcement learning methods can be model-free, interpreting data through constant trial and error, or model-based, following a set of predefined steps with minimal trial and error.
This means that Q-learning algorithms search for the most optimal method of achieving a set goal through maximum rewards. For instance, Google’s DeepMind often uses Q-learning alongside deep learning. A Q-learning algorithm may also consist of backward induction (BIN) or hindsight experience replay (HER).
Model-based value estimation
With model-free approaches like Q-learning, there are fewer possibilities for possible states and actions. Under restricted dynamics, model-based methods can rapidly perform near-optimal control. A model-based method is designed for a specific use case.
Why is Machine Learning Important?
In addition to supporting the development of new products, machine learning gives enterprises a view of trends in customer behavior. Machine learning is central to the operations of many of today’s leading companies, such as Facebook, Google, and Uber. For many companies, machine learning is a key competitive differentiator.