Top 10 Machine Learning Algorithms For 2022

Introduction

We live in a world where all most manual tasks are automated. Today, Machine Learning (ML) algorithms can assist computers in various jobs, from playing chess, to performing surgeries. A vital feature of this revolution is how computing tools and techniques are accessible to everyone. ML can be unsupervised, supervised, semi-supervised, and reinforced. This blog highlights the ten best ML algorithms that ML enthusiasts and data scientists should know in 2022.

Top Ten latest ML algorithms to Explore in 2022

Following are the top ten ML algorithms for 2022.

1. Linear Regression

In a linear regression procedure, one determines the connection between a dependent and an independent variable by fitting them to a line. This line is s a regression line and the linear equation Y=a*X+b represents the line. The coefficients, a and b are obtained by decreasing the sum of the squared difference of distance between the regression line and data points

2. Logistic Regression

The logistic regression algorithm evaluates distinct values from a collection of independent variables. It helps predict the possibility of an event by fitting the data into a logit function. Thus, the algorithm is also known as logit regression.

3. Decision Tree

A decision tree is a well-known ML algorithm. It is a controlled learning algorithm that categorizes problems. This algorithm works well with both categorical dependent and continuous variables.

4. SVM

SVM categorizes algorithms to plot raw data as points in an n-dimensional region. This algorithm divides the population into two or more identical sets depending on the most substantial attributes of independent variables.

5. Naive Bayes Algorithm

The Naive Bayes classifier algorithm presumes that a specific feature in a class has no relation to any other element. While calculating the probability, a Naive Bayes classifier considers all the properties independently even if they bear any relation to each other.

6. KNN

The KNN algorithm applies to both classification and regression problems. This ML algorithm resolves classification problems. KNN is very easy to understand and the only issue with it is that it is expensive and requires pre-processing.

7. K-Means

K-Means is an unsupervised ML algorithm that resolves clustering issues. The algorithm categorizes data into a specific number of clusters. The K-Means algorithm categorizes data so that the data points within a cluster are heterogeneous and homogeneous from data in other clusters.

8. Random Forest Algorithm

Random forest is a set of decision trees that classifies a new object based on its attributes. The algorithm categorizes trees and then the tree votes for that specific class.

9. Dimensionality Reduction Algorithms

Corporations, research organizations, and government agencies store huge amounts of data and information. Raw data contains a lot of information and the challenge lies in detecting significant variables and patterns. Dimensionality reduction algorithms can assist in finding relevant details.

10. Gradient Boosting Algorithm and Ada Boosting Algorithm

Boosting algorithms are ideal for managing huge data loads to make forecasts with high accuracy. Boosting is an ensemble learning algorithm that merges the predictive power of several base estimators to enhance robustness.

Conclusion

Once you read this article, you should not only have a better idea of the various ML models out there, but you should also know when it is appropriate to use these models.