5 Popular Machine Learning algorithms

Last Updated on August 14, 2021 by Editorial Team

Author(s): Or

Machine Learning

A machine learning algorithm is conceptually similar to any other algorithm in computer science. An ML algorithm is a data-driven process that is used to create a production-ready machine learning model.

If you consider machine learning to be a train for accomplishing a job, then machine learning models are the engines that push the train. The most effective machine learning algorithm is determined by the business issue in question, the complexity of the dataset, and the available resources.

Types of Machine Learning algorithms?

Supervised Machine learning Algorithms

Algorithms that use machine learning to generate predictions based on a collection of data. A supervised machine learning algorithm looks for patterns in the data points’ value labels.

Unsupervised Machine learning Algorithms

There are no related labels for the data points. These machine learning algorithms cluster the data to explain its structure and make complex data seem simple and organized for research.

Semi-supervised learning algorithms

Semi-supervised learning is a machine learning technique that involves training on a small quantity of labeled data and a large amount of unlabeled data. Semi-supervised learning is a kind of learning that lies between unsupervised (without labeled training data) and supervised learning (with only labeled training data).

Top Machine Learning Algorithms

1. Linear Regression

The Linear Regression method illustrates the relationship between two variables and the effect of a change in one on the other. The algorithm illustrates the effect of modifying the independent variable on the dependent variable. Independent variables are considered explanatory variables since they provide information about the things that influence the dependent variable. Often, the dependent variable is known as the interest factor or predictor. Linear regression is a technique that is used to estimate actual continuous values. The most frequent applications of linear regression are property price forecasting, sales forecasting, weather forecasting, and employee pay estimates. The fundamental objective of linear regression is to find the best fit line between the predictions. The linear regression equation is Y=a*x+b, where y is the dependent variable and x denotes the collection of independent variables. The slope is denoted by a, while the intercept is denoted by b.

Example:

• Sales Forecasting

Linear regression is often used in business for trend-based sales forecasting. If a business consistently increases its sales month after month, a linear regression study of the monthly sales data enables the business to predict future sales.

• Risk Assessment

Linear regression is helpful for risk assessment in the insurance or financial domains. A health insurance firm may do a linear regression analysis to determine the number of claims per client about their age. This research assists insurance firms in determining that elderly consumers have a higher propensity to file insurance claims. These analysis findings are critical in making critical business choices and are used to account for risk.

2. Logistic Regression

This algorithm uses a logistic function to predict the result of a categorical dependent variable using predictor variables. The probabilities or chances that determine the result of a single experiment are represented as a function of explanatory factors. Based on the provided predictor factors, logistic regression algorithms assist in estimating the likelihood of falling into a certain level of the categorical dependent variable.

Examples:

• In epidemiology, the logistic regression technique estimates risk factors for disease and design appropriate preventative actions.
• They are used to forecast a political election’s outcome or forecast whether a person would vote for a specific candidate.
• It is used to group a collection of words into nouns, pronouns, verbs, and adjectives.
• In weather forecasting, it is used to forecast the likelihood of rain.
• They are used in credit rating systems for risk management purposes to forecast an account’s default.

Sci-Kit Learn is a Python data science library that implements the Logistic Regression Machine Learning Algorithm.

3. Decision Tree

A decision tree is a graphical representation that employs branching to illustrate all potential outcomes of a choice under specified circumstances. The internal node of a decision tree represents an attribute test. Each branch of the tree reflects the result of the test, and the leaf node represents a specific class label, i.e., the decision made after calculating all of the attributes. The route from the root to the leaf node is used to illustrate the classification rules.

Classification trees- These are the most common decision trees used to categorize a dataset depending on the response variable. Generally, they are employed when the answer variable is categorical.

Regression Trees- Regression trees are utilized when the response or target variable is consistent or numerical. In contrast to classification, they are often employed in prediction problems.

Additionally, decision trees may be divided into two kinds according to the target variable’s type: Continuous Variable Decision Trees and Binary Variable Decision Trees. The target variable is used to determine the kind of decision tree needed for a specific issue.

Examples

• Decision trees are a typical machine learning algorithm that is very useful in finance for option pricing.
• Remote sensing is a field of application for decision tree-based pattern identification.
• Banks employ decision tree algorithms to categorize loan applicants based on their likelihood of defaulting on payments.

4. Naive Bayes Algorithm

The Naive Bayes Classifier is one of the most common machine learning techniques categorized by similarities. It is based on the well-known Bayes Theorem of Probability and is used to develop machine learning models for disease prediction and document categorization. It is a precise categorization of words based on the Bayes Probability Theorem for subjective content analysis. This classification algorithm makes use of the Bayes theorem to calculate probabilities. The fundamental premise behind Naive Bayesian algorithms is that all characteristics are regarded as independent of one another. It is a fairly straightforward method that is simple to implement. It is especially advantageous for big datasets and may be used with text datasets.

Examples

• Facebook uses sentiment analysis to determine if a status post expresses positive or negative feelings.
• Document Categorization- Google utilizes document classification to index documents and determines their relevance ratings, referred to as the PageRank. PageRank considers pages that have been designated as significant in databases that have been processed and categorized using a document classification method.
• The Naive Bayes algorithm is also used to categorize news items on Technology, Entertainment, Sports, and Politics.
• Email Spam Filtering-Google Mail utilizes the Naive Bayes algorithm to determine if an email is spam or not.

5. Artificial Neural Network Algorithm

It is an area in artificial intelligence based on brain simulations. It is a kind of computational network made up of neurons that are linked to one another. This linked structure is used to provide a variety of predictions for regression and classification issues. The ANN comprises many layers: an input layer, a hidden layer, and output layers. The number of hidden layers may be more than one. The hidden layer is where the neural network’s calculations take place. The actual weights and biases equations are included here, as well as the application of activation functions. These activation functions are critical for structuring and trimming the output and are mainly used to solve nonlinear problems such as handwriting recognition. ANNs are computationally expensive because they need sophisticated mathematical computations.

Examples

• Financial institutions use machine learning algorithms to improve loan application evaluation, bond rating, target marketing, and credit score. They are also used to detect cases of credit card fraud.
• Buzzfeed organizes and searches videos and pictures using artificial neural network techniques for image recognition.
• Google employs Artificial Neural Networks for speech recognition, image identification, and other pattern recognition applications (such as handwriting recognition). ANNs are used by Google to detect spam and for a variety of other purposes.
• Artificial Neural Networks are ideal for changing temperature settings, operating equipment, and diagnosing faults in robotic factories.

Final Thoughts

In this article, we have discussed various types of machine learning and their associated algorithms. Machine learning is critical because it provides businesses with insight into trends in consumer behavior and operational patterns and helps create new products. Today, several major businesses, like Facebook, Google, and Uber, have made machine learning a key component of their operations. For many businesses, machine learning has become a significant competitive difference.

Algorithms are used to identify patterns, and even the most seasoned data scientist cannot predict which algorithm would perform the best without trying many different techniques first. While there are many more Machine Learning algorithms, the ones described in this article are the most commonly utilized.

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Published via Towards AI