Top 10 Machine Learning Algorithms for 2024

Machine Learning Algorithms: Machine learning (ML) can analyze X-rays, predict stock market prices, and recommend binge-worthy television shows. The global machine learning market is expected to grow significantly from $21.7 billion in 2022 to $209.91 billion by 2029 due to its wide range of applications.

This article will guide you to 10 popular machine learning algorithms and explain the various learning styles for creating functioning machine learning models.

10 machine learning algorithms to know

A machine learning algorithm is, in simple terms, a recipe that enables computers to learn and make predictions based on data. We provide the computer with a large quantity of data and allow it to discover patterns, relationships, and insights on its own instead of giving it explicit instructions.

From classification to regression, here are ten machine learning algorithms you should be familiar with.

1. Apriori

The Apriori algorithm analyzes transactional data in a relational database. It finds groups of items that are commonly bought together. The item sets are used to create association rules. If customers often purchase products A and B together, we can create an association rule suggesting that buying A increases the chances of buying B.

The Apriori algorithm helps analysts find helpful information from transactional data. It allows them to make predictions or recommendations based on patterns observed in group items.

2. Naive Bayes

It is a supervised learning algorithm used to make predictive models for tasks involving binary or multi-classification. Bayes’ Theorem is used in this method, which calculates the probability of classification by considering the conditional probabilities. It assumes that the factors involved are independent of each other.

Imagine a program that can identify plants using a simple Naive Bayes algorithm. The algorithm categorizes plant images based on size, color, and shape. The algorithm considers each aspect separately but combines them to determine the likelihood of an object being a specific plant.

Naive Bayes uses the assumption of independence among factors to simplify calculations and work efficiently with large datasets. It works excellently for tasks like sorting documents, filtering spam emails, analyzing sentiments, and other applications where factors can be considered individually but still affect the overall classification.

3. Decision tree

A decision tree is an algorithm used for classification and predictive modeling. It is a supervised learning method. It’s like a flowchart that begins with a primary question about the data. The data is sent down different branches to internal nodes. These nodes ask more questions and guide the data to more branches. The process continues until the data reaches a leaf node, where no more branches exist.

Decision tree algorithms are mostly used in machine learning (ML) because they can effectively handle complex datasets straightforwardly. The algorithm’s structure is easy to understand and interpret for decision-making. Decision trees allow us to classify or predict outcomes by asking questions and following different paths based on the data’s characteristics.

Decision trees are useful in machine learning because they are simple and easy to understand. They are beneficial when working with complicated datasets.

4. Linear regression

Linear regression is a type of algorithm used in supervised learning. It helps predict and forecast values within a continuous range, like sales numbers or housing prices. Linear regression is a statistical technique to find a relation between an input variable (X) and an output variable (Y). A straight line typically represents this relationship.

Linear regression is a method that finds the best-fitting line for a set of data points with known input and output values. The “regression line” is a predictive model. We can use this line to estimate or predict the output value (Y) for a given input value (X).

Linear regression is mainly used for making predictions rather than classifying data. Understanding the impact of input changes on the output is helpful. By looking at the slope and intercept of the regression line, we can understand how the variables are related and use this information to make predictions.

5. Logistic regression

Logistic regression, called “logit regression,” is a supervised learning algorithm mainly used for binary classification tasks. We often use it to determine if something belongs to a specific group, like deciding if a picture is of a cat.

Logistic regression is commonly used for binary categorization instead of predictive modeling. We can use it to categorize input data into two classes by considering the probability estimate and a specific threshold. Logistic regression is useful for tasks like image recognition, spam email detection, or medical diagnosis. It helps categorize data into different classes.

6. K-means

K-means is a popular algorithm for clustering and pattern recognition. It falls under unsupervised learning. The goal is to group data points based on how close they are to each other. K-means is like K-nearest neighbor (KNN) because it uses proximity to find patterns or clusters in data.

Clusters are defined by a centroid, which is a center point for the cluster. K-means is great for clustering large data sets but may need help with outliers.

K-means is excellent for big datasets and helps find patterns by grouping similar points. It can be used in different areas like dividing customers, compressing images, and detecting anomalies.

7. Random forest

A random forest algorithm is a group of decision trees used together for classification and predictive modeling. A random forest is a method that improves prediction accuracy by merging the predictions of multiple decision trees instead of relying on just one decision tree.

In a random forest, many decision tree algorithms (sometimes hundreds or even thousands) are trained individually using random samples from the training dataset. The sampling method is called “bagging.” Each decision tree is trained separately on its random sample.

After training, the random forest uses the exact data for each decision tree. Every tree makes a prediction, and the random forest adds up the results. The final prediction for the dataset is determined by selecting the most common forecast among all the decision trees.

Random forests solve the problem of “overfitting” that can happen with single decision trees. Overfitting occurs when a decision tree becomes too focused on its training data, causing it to be less accurate when given new data.

8. K-nearest neighbor (KNN)

K-nearest neighbor (KNN) is a famous classification and predictive modeling algorithm. It is a supervised learning method. The “K-nearest neighbor” algorithm classifies an output by looking at how close it is to other data points on a graph.

Imagine a dataset with labeled points, where some are marked as blue and others as red. To classify a new data point using KNN, we examine its closest neighbors on the graph. The “K” in KNN stands for the number of nearest neighbors that are taken into consideration. If K is set to 5, the algorithm considers the five nearest points to the new data point.

The algorithm assigns a classification to the new data point based on most labels among its K nearest neighbors. The algorithm will classify the unique point as part of the blue group if most of the closest neighbors are blue points.

KNN can be used for prediction tasks too. KNN can estimate the value of an unknown data point by using the average or median of its K nearest neighbors instead of assigning a class label.

9. Support vector machine (SVM)

A support vector machine (SVM) algorithm is used for classification and predictive modeling. It is a supervised learning method. SVM algorithms are famous for their reliability and ability to perform well with limited data. SVM algorithms create a decision boundary called a “hyperplane.” In 2D space, a hyperplane is like a line separating two labeled data sets.

SVM aims to find the optimal decision boundary by maximizing the margin between two labeled data sets. It searches for the largest space between the classes. New data points are classified based on the labels in the training dataset, depending on which side of the decision boundary they fall on.

Hyperplanes in three-dimensional space can have different shapes, which helps SVM handle complex patterns and relationships in the data.

10. Gradient boosting

Gradient boosting algorithms use an ensemble method to create a robust predictive model. They start with weak models and iteratively improve them. The iterative process helps models improve over time, resulting in an optimal and accurate final model.

The algorithm begins with a simple model that makes basic assumptions, like classifying data as above or below the mean. The first model is a starting point.

The algorithm creates a new model in each iteration to fix the mistakes made by the previous models. The new model incorporates patterns or relationships that previous models couldn’t capture.

Gradient boosting is excellent for solving challenges and working with big datasets. Ensemble models can capture complex patterns and relationships that a single model might overlook. Gradient boosting is a method that creates a strong predictive model by combining predictions from multiple models.

Get started in machine learning

Learn how to build and deploy machine learning models, analyze data, and make informed decisions with hands-on projects and interactive exercises. By gaining confidence in applying machine learning in different areas, you can unlock exciting career opportunities in data science. Also read the best Al Marketing Assistants for Business.

Exit mobile version