6 Machine Learning Algorithms You Should Know In 2021
Machine learning is used to build applications that learn from data and improve their accuracy without being programmed to do so. It is a branch of artificial intelligence (AI) and the application of machine learning can be seen across industries.
Algorithms play a crucial role in AI development services. Algorithms are the set of rules followed by machines or applications when carrying out problem-solving operations. Various algorithms are used depending on the purpose of the application and the desired outcome and these are machine learning algorithms you should know in 2021.
- Linear regression and logistic regression
Regression is an approach that is used in machine learning where the target value is modelled based on independent predictors and can be used to find the cause and effect relationship between variables. It is usually used for forecasting and there are various regression techniques used in AI development services.
In linear regression, there is only one independent variable and the relationship between the independent and dependent variable is linear. A simpler explanation of linear regression is that the relationship between the independent and dependent variables is established by fitting them on a straight line.
Logistic regression can be described as an S-curve, with two maximum value predictions. Like linear regression, it is a popular algorithm used by any machine learning consultancy. This technique can be used to predict the categorical dependent variable using the given set of independent variables.
While linear regression is used to solve regression problems, logistic regression is used to solve classification problems. However, both techniques are among the machine learning algorithms you should know in 2021.
- K-nearest neighbours
Referred to as lazy learning by some, K-nearest neighbours or KNN uses a majority voting mechanism. The algorithm collects data from a training dataset and then uses the data to make predictions for new records.
When it comes to the new record, the k-closest records of the training dataset are determined and a prediction is made for the new record based on the value of the target attribute of the closest records.
AI service providers like using the KNN algorithm because it makes highly accurate predictions and is ideal for applications that do not require a human-readable model.
- Decision tree
Decision tree is another machine learning algorithm that you should know in 2021. It is used by AI service providers to classify both categorical and continuous dependent variables. Decision tree is a supervised learning algorithm and there are two types of decision trees; Classification trees and regression trees.
The algorithm is comprehensive and specific while also being simple.
- Naive Bayes
The Naive Bayes algorithm is used to classify data based on conditional probability values computation and is based on Bayes’ Theorem. The theorem describes the probability of occurrence of an event related to a condition. It is also used in conditional probability.
In machine learning, the Naive Bayes algorithm is ideal for application in real-time prediction, multi-class prediction, and text classification. The benefits of using the algorithm include ease of implementation, speed, and scalability. Naive Bayes algorithm also requires less training.
- SVM
Support Vector Machine or SVM is a supervised learning algorithm used for classification and regression problems. The algorithm creates the best decision boundary to divide n-dimensional space into classes. This makes it easier to put new datasets into the correct category in the future.
In SVM, the best decision boundary is called a hyperplane and, when creating it, the algorithm chooses support vectors or the extreme vectors that help in its creation.
There are two types of SVM algorithms; Linear SVM and non-linear SVM. The type of SVM used depends on if the dataset can be classified by using a straight line.
- Random forest
In the random forest algorithm, multiple decision trees are combined to reach a single result. The algorithm can be used for both classification and regression problems and this flexibility, along with its ease of use, is one of the main reasons it is widely used.
In addition to these benefits, there is no risk of overfitting, unlike the decision tree algorithm, and the random forest algorithm also makes it easy to evaluate variable importance to the model.
