A machine learning algorithm is a program code (math or program logic) that enables professionals to study, analyze, comprehend and explore large complex datasets. This article explains the fundamentals of machine learning algorithms and reveals the top 10 machine learning algorithms in 2022.
Table of Contents
- What Is a Machine Learning Algorithm?
- Top 10 Machine Learning Algorithms in 2022
A machine learning algorithm refers to a program code (math or program logic) that enables professionals to study, analyze, comprehend, and explore large complex datasets. Each algorithm follows a series of instructions to accomplish the objective of making predictions or categorizing information by learning, establishing, and discovering patterns embedded in the data.
Machine learning algorithms specify rules and processes that a system should consider while addressing a specific problem. These algorithms analyze and simulate data to predict the result within a predetermined range. Moreover, as new data is fed into these algorithms, they learn, optimize, and improve based on the feedback on previous performance in predicting outcomes. In simple words, machine learning algorithms tend to become ‘smarter’ with each iteration.
Depending on the type of algorithm, machine learning models use several parameters such as gamma parameter, max_depth, n_neighbors, and others to analyze data and produce accurate results. These parameters are a consequence of training data that represents a larger dataset.
Machine learning algorithms are classified into four types based on the learning techniques: supervised, semi-supervised, unsupervised, and reinforcement learning. Regression and classification algorithms are the most popular options for predicting values, identifying similarities, and discovering unusual data patterns.
1. Supervised learning
Supervised learning algorithms use labeled datasets to make predictions. This learning technique is beneficial when you know the kind of result or outcome you intend to have.
For example, consider that you have a dataset that specifies the rain that occurred in a geographic area during a particular season over the past 200 years. You intend to know the expected rain during that specific season for the next ten years. Here, the outcome is derived based on the labels existing in the original dataset, i.e., rainfall, geographic area, season, and year.
2. Unsupervised learning
Unsupervised learning algorithms use unlabeled data. This learning technique labels the unlabeled data by categorizing the data or expressing its type, form, or structure. This technique comes in handy when the result type is unknown.
For example, when you use a dataset of Facebook users, you intend to classify users who show inclination (based on likes) toward similar Facebook ad campaigns. In this case, the dataset is unlabeled. However, the result will have labels as the algorithm will find similarities between data points while classifying the users.
3. Semi-supervised learning (SSL)
Semi-supervised learning algorithms combine the above two, where labeled and unlabeled data are used. The objective of these algorithms is to categorize unlabeled data based on the information derived from labeled data.
Consider the example of web content classification. Categorizing and classifying the content available on the internet is a time- and resource-intensive task. Apart from AI algorithms, it requires human resources to organize billions of web pages available online. In such cases, SSL models can play a crucial role in accomplishing the task efficiently.
4. Reinforcement learning
Reinforcement learning algorithms use the result or outcome as a benchmark to decide the next action step. In other words, these algorithms learn from previous outcomes, receive feedback after every step, and then decide whether to go ahead with the next step or not. The system learns whether it made a right, wrong, or neutral choice in the process. Automated systems can employ reinforcement learning as they are designed to make decisions with minimal human intervention.
For example, you design a self-driving car and intend to track whether the car is following traffic rules and ensuring safety on the roads. By applying reinforcement learning, the vehicle learns through experience and reinforcement tactics. The algorithm ensures that the car obeys traffic laws of staying in one lane, follows speed limits, and stops encountering pedestrians or animals on the road.
See More: What Is Artificial Intelligence (AI) as a Service? Definition, Architecture, and Trends
Machine learning has significantly impacted our daily lives. Machine learning is omnipresent from smart assistants scheduling appointments, playing songs, and notifying users based on calendar events to NLP-based voice assistants. All such intelligent systems operate on machine learning algorithms.
In data science, each machine learning algorithm handles a specific problem. In some cases, professionals tend to opt for a combination of these algorithms as one algorithm may not be able to solve a particular problem.
Here, we look at the top 10 machine learning algorithms that are frequently used to achieve actual results.
1. Linear regression
Linear regression gives a relationship between input (x) and an output variable (y), also referred to as independent and dependent variables. Let’s understand the algorithm with an example where you are required to arrange a few plastic boxes of different sizes on separate shelves based on their corresponding weights.
The task is to be completed without manually weighing the boxes. Instead, you need to guess the weight just by observing the boxes’ height, dimensions, and sizes. In short, the entire task is driven based on visual analysis. Thus, you have to use a combination of visible variables to make the final arrangement on the shelves.
Linear regression in machine learning is of a similar kind, where the relationship between independent and dependent variables is established by fitting them to a regression line. This line has a mathematical representation given by the linear equation y = mx + c, where y represents the dependent variable, m = slope, x = independent variable, and b = intercept.
The objective of linear regression is to find the best fit line that reveals the relationship between variables y and x.
2. Logistic regression
The dependent variable is of binary type (dichotomous) in logistic regression. This type of regression analysis describes data and explains the relationship between one dichotomous variable and one or more independent variables.
Logistic regression is used in predictive analysis where pertinent data predict an event probability to a logit function. Thus, it is also called logit regression.
Mathematically, logistic regression is represented by the equation:
y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))
x = input value, y = predicted output, b0 = bias or intercept term, b1 = coefficient for input (x).
Logistic regression could be used to predict whether a particular team will win (1) the FIFA World Cup 2022 or not (0), or whether a lockdown will be imposed (1) due to rising COVID-19 cases or not (0). Thus, the binary outcomes of logistic regression facilitate faster decision-making as you only need to pick one out of the two alternatives.
3. Decision trees
With a decision tree, you can visualize the map of potential results for a series of decisions. It enables companies to compare possible outcomes and then take a straightforward decision based on parameters such as advantages and probabilities that are beneficial to them.
Decision tree algorithms can potentially anticipate the best option based on a mathematical construct and also come in handy while brainstorming over a specific decision. The tree starts with a root node (decision node) and then branches into sub-nodes representing potential outcomes.
Each outcome can further create child nodes that can open up other possibilities. The algorithm generates a tree-like structure that is used for classification problems. For example, consider the decision tree below that helps finalize a weekend plan based on the weather forecast.
4. Support vector machines (SVMs)
Support vector machine algorithms are used to accomplish both classification and regression tasks. These are supervised machine learning algorithms that plot each piece of data in the n-dimensional space, with n referring to the number of features. Each feature value is associated with a coordinate value, making it easier to plot the features.
Moreover, classification is further performed by distinctly determining the hyper-plane that separates the two sets of support vectors or classes. A good separation ensures a good classification between the plotted data points.
In simple words, SVMs represent the coordinates for individual observations. These are popular machine learning classifiers used in applications such as data classification, facial expression classification, text classification, steganography detection in digital images, speech recognition, and others.
5. Naive Bayes algorithm
Naive Bayes refers to a probabilistic machine learning algorithm based on the Bayesian probability model and is used to address classification problems. The fundamental assumption of the algorithm is that features under consideration are independent of each other and a change in the value of one does not impact the value of the other.
For example, you can consider a ball, a cricket ball, if it is red, round, has a 7.1-7.26 cm diameter, and has a mass of 156-163 g. Although all these features could be interdependent, each one contributes to the probability that it is a cricket ball. This is the reason the algorithm is referred to as ‘naïve’.
Let’s look at the mathematical representation of the algorithm.
If X, Y = probabilistic events, P (X) = probability of X being true, P(X|Y) = conditional probability of X being true in case Y is true.
Then, Bayes’ theorem is given by the equation:
P (X|Y) = (P (Y|X) x P (X)) /P (Y)
A naive Bayesian approach is easy to develop and implement. It is capable of handling massive datasets and is useful for making real-time predictions. Its applications include spam filtering, sentiment analysis and prediction, document classification, and others.
6. KNN classification algorithm
The K Nearest Neighbors (KNN) algorithm is used for both classification and regression problems. It stores all the known use cases and classifies new use cases (or data points) by segregating them into different classes. This classification is accomplished based on the similarity score of the recent use cases to the available ones.
KNN is a supervised machine learning algorithm, wherein ‘K’ refers to the number of neighboring points we consider while classifying and segregating the known n groups. The algorithm learns at each step and iteration, thereby eliminating the need for any specific learning phase. The classification is based on the neighbor’s majority vote.
The algorithm uses these steps to perform the classification:
- For a training dataset, calculate the distance between the data points that are to be classified and the rest of the data points.
- Choose the closest ‘K’ elements based on the distance or function used.
- Consider a ‘majority vote’ between the K points–the class or label dominating all data points reveals the final ranking.
Real-life applications of KNN algorithms include facial recognition, text mining, and recommendation systems such as Amazon, Netflix, and others.
K-Means is a distance-based unsupervised machine learning algorithm that accomplishes clustering tasks. In this algorithm, you classify datasets into clusters (K clusters) where the data points within one set remain homogenous, and the data points from two different clusters remain heterogeneous.
The clusters under K-Means are formed using these steps:
- Initialization: The K-means algorithm selects centroids for each cluster (‘K’ number of points).
- Assign objects to centroid: Clusters are formed with the closest centroids (K clusters) at each data point.
- Centroid update: Create new centroids based on existing clusters and determine the closest distance for each data point based on new centroids. Here, the position of the centroid also gets updated whenever required.
- Repeat: Repeat the process till the centroids do not change.
K-Means clustering is useful in applications such as clustering Facebook users with common likes and dislikes, document clustering, segmenting customers who buy similar ecommerce products, etc.
8. Random forest algorithm
Random forest algorithms use multiple decision trees to handle classification and regression problems. It is a supervised machine learning algorithm where different decision trees are built on different samples during training. These algorithms help estimate missing data and tend to keep the accuracy intact in situations when a large chunk of data is missing in the dataset.
Random forest algorithms follow these steps:
- Select random data samples from a given data set.
- Build a decision tree for each data sample and provide the prediction result for each decision tree.
- Carry out voting for each expected result.
- Select the final prediction result based on the highest voted prediction result.
This algorithm finds applications in finance, ecommerce (recommendation engines), computational biology (gene classification, biomarker discovery), and others.
9. Artificial neural networks (ANNs)
Artificial neural networks are machine learning algorithms that mimic the human brain (neuronal behavior and connections) to solve complex problems. ANN has three or more interconnected layers in its computational model that process the input data.
The first layer is the input layer or neurons that send input data to deeper layers. The second layer is called the hidden layer. The components of this layer change or tweak the information received through various previous layers by performing a series of data transformations. These are also called neural layers. The third layer is the output layer that sends the final output data for the problem.
ANN algorithms find applications in smart home and home automation devices such as door locks, thermostats, smart speakers, lights, and appliances. They are also used in the field of computational vision, specifically in detection systems and autonomous vehicles.
10. Recurrent neural networks (RNNs)
Recurrent neural networks refer to a specific type of ANN that processes sequential data. Here, the result of the previous step acts as the input to the current step. This is facilitated via the hidden state that remembers information about a sequence. It acts as a memory that maintains the information on what was previously calculated. The memory of RNN reduces the overall complexity of the neural network.
RNN analyzes time series data and possesses the ability to store, learn, and maintain contexts of any length. RNN is used in cases where time sequence is of paramount importance, such as speech recognition, language translation, video frame processing, text generation, and image captioning. Even Siri, Google Assistant, and Google Translate use the RNN architecture.
See More: What Is Logistic Regression? Equation, Assumptions, Types, and Best Practices
Machine learning algorithms tend to learn from observations. They analyze data, map input to output, and detect data patterns. The algorithms become smarter as they process more data, improving overall predictive performance.
Depending on the changing requirements and the complexity of the problems, new variants of existing machine learning algorithms continue to emerge. You can choose the algorithm that best suits your needs and get a head start on machine learning.