Top 10 Machine Learning Algorithms You Need to Know in 2022

Top 10 Machine Learning Algorithms You Need to Know in 2022

In a world where the definition of manual is transforming to automation, the Machine Learning (ML) algorithm makes the word 'Impossible' a reality. Technology can help computers play chess, perform surgeries, and even become more personal and innovative.

Living in a period of constant technological progress, computing has evolved drastically over the years; hence, we can predict what's to come shortly.

A primary aspect of this upgrade is how computing tools and methods have been democratized. Over the last five years, data scientists have built trailblazing data-crunching machines by optimally functioning advanced techniques. The results have been phenomenal.

If you're one of the data scientists or ML aspirants, then you should get a catch of Machine Learning algorithms.

In this article, we'll discuss the top 10 Machine Learning Algorithms you need to know in 2022, so let's get started.

Top 10 Machine Learning Algorithms

1. Linear Regression

This process establishes the relationship between dependent and independent variables by fitting them to a line. This line is called the regression line and is represented using an equation:

Y= a *X + b

Where,

Y = Dependent Variable

a = Slope

X = Independent variable

b = Intercept

The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and regression line.

2. Logistic Regression

This process estimates the discrete values from an independent variable set. It helps predict the probability of an event by fitting data to a logit function, called logit regression.

The methods that can help enhance the logistic regression include interaction terms, a non-linear model, eradicating features, and regularizing techniques.

3. Decision Tree

This is one of the widely popular and used algorithms of Machine learning. It's a supervised learning algorithm that is used for classifying problems. It works with the classification of both continuous and categorical dependent variables.

In the Decision Tree algorithm, we split the population into two/more homogeneous sets based on the relevant attributes.

4. Support Vector Machine (SVM)

This process is used to classify algorithms in which you plot raw data as points in an n-dimensional space. The value of each attribute is then tied to a specific coordinate, making it seamless to classify the data.

Lines known as classifiers can be leveraged to plot the data and plot them on a graph.

5. Naïve Bayes

This classifier assumes that the presence of a specific feature in a class is unrelated to the presence of any other attribute. Even if these features are related, a Naive Bayes algorithm would consider all of these factors independently when calculating the probability of a specific outcome.

Moreover, this algorithm is easy to develop and is helpful for colossal datasets. A simple Naive Bayesian model is known to outperform highly sophisticated classification techniques.

6. K-Nearest Neighbors (KNN)

KNN is a more widely leveraged algorithm to solve classification problems; however, this technique can also be applied to regression problems. This simple algorithm stores all available cases and classifies new cases by taking a majority vote of its k-neighbors.

The case is then given to the class with the most similarity. Finally, a distance function executes this measurement.

Though the KNN has some perks; however, you must be exceptionally considerate on selecting KNN as they're computationally expensive, its variables must be normalized (else higher range variables can bias the model), and the data requires pre-processing.

7. K-Means

K-Means is an unsupervised learning algorithm that solves clustering problems. Datasets are classified into a certain number of clusters so that all the data points within a cluster are hetero and homogeneous from other cluster data.

Let's see how K-Means form clusters.

• The K-Means algorithm picks the 'k' number of points known as centroid for each cluster.
• Each data point forms a cluster with the nearest centroids (K clusters)
• Now, it creates new centroids based on the existing cluster members.
• The nearest distance for each data point is estimated with the new centroids. This process continues until the centroids don't change.

8. Random Forest Algorithm

A collective of decision trees is known as a Random Forest, which is leveraged to classify a new object based on its attributes, where each tree is classified, and the tree votes for that class. The forest selects the classification having the most votes.

How are these trees planted and grown?

• If the number of cases is 'N' in the training set, the sample of N cases is taken randomly. This sample will be the training set for tree growing.
• Suppose there are 'M' input variables, a number m << M is specified such that at each node, 'm' variables are chosen randomly from the 'M,' and the best split on this 'm' is leveraged to split the node. The value of 'm' is held constant during this process.
• Each tree is grown to the most substantial extent possible, and there's no pruning.

9. Dimensionality Reduction

In the modern world, a massive amount of data is being analyzed and stored by govt agencies, research organizations, and corporates. If you're a data scientist, you know that this raw data contains several pieces of information, and the quest is to determine relevant patterns and variables.

To determine essential details, Dimensionality Reduction such as Factor Analysis, Random Forest, Decision Tree, and Missing Value Ration can be helpful.

10. Gradient Boosting & AdaBoosting

These boosting algorithms are leveraged when colossal data loads have to be handled to create predictions with top-notch accuracy. Boosting is an ensemble learning algorithm that merges the predictive power of many base estimators to enhance robustness.

These algorithms work top-class in data science competitions such as CrowdAnalytix, Kaggle, and AV Hackathon. These are the most preferred ML algorithms today and can be leveraged along with R Codes and Python to achieve precise results.

Concluding Thoughts

If you want to develop a career in ML, start now. The field is sky-rocketing, and the sooner you understand the scope of ML techniques, the better you'll be able to offer solutions to challenging workplace issues.

However, if you're looking forward to becoming one, enroll in our ML certification training course to learn new software trends, emerging techniques, customizing considerations, and core competencies needed by a Machine Learning expert.

By opting for iCert Global's certification training course, we ensure that you ace the role of a Machine Learning professional in any organization.