10 Popular Machine Learning Algorithms: An In-Depth Look

As machine learning makes up many services worldwide, machine learning systems or models have become beneficial to us. Thus, this article will cover and further elaborate on things like the algorithm of machine learning to make it become known to the general public what machine learning is all about and what its popular algorithms are. As an algorithm is basically the core of machine learning, it is trained on various data sets, which later on will become and be developed into machine learning models that power many services or system innovations in the world. From a simple application to product recommendation, machine learning has many uses and functions that are leveraged and applied in the real-world setting without us even knowing. Thus, this article will feature ten popular machine learning algorithms to help readers fully grasp the wide use and application of machine learning.

Part 1. What is Machine Learning Algorithms?

Machine Learning Algorithm is like an ingredient or recipe that allows a system to learn and generate predictions based on data, a key concept in general AI. Machine Learning Algorithm works by working on a large amount of data to discover and find patterns, relationships, and even insights– we users are only there to provide data instead of telling the system to make insights, discover patterns, and such.

As it is set on various procedures and mathematical rules, the system makes predictions and decisions without having it explicitly programmed, and these algorithms improve over time as the system is exposed to more data.

Part 2. 10 Popular Machine Learning Algorithms

1. Linear Regression

Linear Regression is a type of Supervised Machine Learning in which its function is for forecasting and predicting values within a range. It takes a set of data points and finds the best fit of the data points with the known input and output values. Moreover, Linear Regression's main function is for predictive modelling rather than for categorization of data. As it is used for predicting continuous outcomes, it became useful when trying to understand the changes in the variable, and through it, the user can gain insight and make predictions on the relationship between the input and output variables.

2. Logistic Regression

Logistic Regression, also known as ‘Logit Regression’, is a type of Supervised Machine Learning Algorithm that is mainly used for the classification of binary tasks. Unlike Linear Regression, Logistic Regression is commonly used to classify or determine if an input belongs to a certain class by estimating the probabilities using a logistic function.

Moreover, as Logistic Regression leans towards predicting the probability of an input in practice, it uses two groups to sort the input into the primary class and the non-primary class. Furthermore, as its main function is more towards categorization rather than being a predictive model, Logistic Regression is a perfect tool for handling image recognition, spam email detection, and medical diagnosis tasks that require categorizing data into certain classes.

3. Naive Bayes

Naive Bayes is also a type of Supervised Learning Algorithm that is capable of handling multi-classification tasks or creating a predictive model for binary classification. This Machine Learning Algorithm is basically based on Bayes’ Theorem that operates on conditional probabilities, in which it treats all properties and inputs independently when calculating the probability of a certain outcome. This makes it useful for handling large datasets, as it is known for being simple yet can outperform other powerful classification methods.

4. Decision Tree

A Decision Tree is also a type of Supervised Machine Learning Algorithm that is used for both predictive modelling and classification tasks. As the name suggests, a decision tree resembles a flowchart that branches out directed down based on the answer it receives about the data. This will then continuously branch out until the data reaches an end note, where no further branching occurs. Decision Tree Algorithm is a popular machine learning algorithm as it can easily handle complex data, providing a straightforward approach to understanding and interpreting the decision-making process of various datasets.

5. Random Forest

Random Forest Algorithm is just like the Decision Tree Algorithm, which is used for predictive modelling and classification. What makes the Random Forest Algorithm different is that instead of just using one decision tree, it uses multiple decision trees that simultaneously branch out and are directed down until they reach an end node to have more options and a variety of accurate predictions of data. Moreover, the multiple Decision Tree Algorithms in the Random Forest are all individually trained using various random training datasets, each of which comes with different and independent predictions, which makes it easy to produce the most accurate tallies of data, as it has multiple Decision Tree Algorithms.

6. K-Nearest Neighbor (KNN)

K-Nearest Neighbour (KNN) is also a Supervised Learning algorithm that is used for predictive modelling and classification tasks. This algorithm is set to have a unique approach to dealing with things, as it is based on the proximity of the data on a graph. KNN will classify data points by looking at their nearest neighbour on the graph, which makes it a perfect algorithm for dealing with classification needs.

7. K-Means

K-Means is an Unsupervised Machine Learning Algorithm which is mainly used for pattern recognition and clustering tasks. Unlike K-Nearest Neighbor (KNN), K-Means aims to group data based on proximity to one another. The K-Means algorithm leverages the concept of proximity to categorize and identify patterns in data sets. Furthermore, by grouping similar points via the K-Means proximity-based algorithm, it can provide a data insight that has many applications in various fields.

8. Support Vector Machine (SVM)

The Support Vector Machine (SVM) is also categorized as a Supervised Machine Learning Algorithm that mainly functions as a predictive modeling and classification algorithm. SVM Algorithm works by creating a decision boundary called a Hyperplane, which is a line that separates and distinguishes two sets of data, making the SVM Algorithm popular as it is reliable and can work with small to average amounts of data. Moreover, as the SVM Algorithm hopes to find the best decision boundary by maximizing the hyperplane set on the data, it will look for the gap between the classes.

9. Apriori

Apriori is a Unsupervised Machine Learning Algorithm that is mainly used for predictive modelling tasks. As a pattern recognition and prediction task algorithm, its uses are for understanding the consumer's likelihood and preferences for purchasing. It examines transactional data and stores it in a database, which the Apriori Algorithm will identify itemsets, and these will then be used to generate association rules. By integrating the Apriori Algorithm in a system, it can uncover insights from transactional data, which will enable the analyst to have a prediction or recommendation of the patterns that the item set association observed.

10. Gradient Boosting

The Gradient Boosting Algorithm is used when there is a lot of massive data to be handled to make predictions with accuracy. It combines numerous weak to average predictors to come up with a stronger and more accurate predictor. The process will gradually reduce the errors made as the iterative process works by creating a series of weak models that, over time, improve, resulting in generating an optimal and accurate model. The iterative process will start with a simple model that takes up basic assumptions, then it will classify the data, which basically serves as the starting point for classifying the data, until the algorithm reaches the end point.

Part 3. FAQs about Popular Machine Learning Algorithms

How do I choose the right algorithm?

Choosing the right Algorithm will really depend on many factors such as the type of problem the user tries to fix, the dataset size to feed into the algorithm, the features and complexity of it, training time constraint and more. There is no such thing as choosing the ‘best,’ but rather it is more about the functionality and performance of the algorithm.

What algorithm works well with small datasets?

The Algorithms that work best with a small set of data are the K-Nearest Neighbour, SVM, Logistic Regression, and NaiveBayes. These represent typical examples of narrow AI applications. However, while they work perfectly fine in handling them, it can also be said that there are restrictions and limitations in using them.

Are neural networks always the best choice?

Not all the time, while it is true that they can be a powerful type of algorithm to use, they tend to be quite demanding in terms of data requirements for training, more computation is needed, and they also tend to interpret a lot harder compared to simpler models.

Why do ensemble methods perform better?

Ensemble methods perform better simply because of their capability to combine multiple models to easily reduce overfitting and improve accuracy, which covers the weakness of many simple algorithms.

Conclusion

This article not only discusses the types of machine learning algorithms but also defines what is a machine learning about. While it is said that there are many types of Machine Learning Algorithms, this article has identified ten popular algorithms, but not limited to. Each algorithm, depending on its functions, does well in its own strengths in terms of organizing data, making predictions, and more.