Machine learning is a revolutionary technology that revolutionizes industries and brings new ideas to many spheres, including healthcare, finances, and entertainment. At the center of this revolution is the capability of using algorithms to create machine learning, in which a system can analyze specific data and then make a prediction or decision for itself. However, the machine learning algorithms are not working well. There are many different algorithms to use, and depending on the type of work, it is best to use an algorithm that is best for this type of work.
Machine learning has a diverse and fluid landscape, from supervised learning, where the models work with already tagged data, to unsupervised learning, where the data is untagged but patterns are detected. In this section, we will focus on the major categories of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning; we will also explain how each of them functions, in which sphere they are used, and their strengths and weaknesses. It is essential to grasp these basic concepts to make the most of precise and sophisticated machine learning applications.
Types of Machine Learning algorithms
There are a few different ways to classify different machine learning algorithms. Still, generally speaking, we may categorize them according to their intended use, and the basic categories are as follows:
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reward-Based Learning
Supervised Learning
In supervised learning, the computer receives instructions via modeling. The operator supplies the machine learning algorithm with a known dataset comprising requisite inputs and outputs; the system then has to determine how to access those inputs and outputs. It is familiar with the pertinent answers to the problem, even while the algorithm discerns patterns in the data, acquires knowledge through observations, and offers forecasts. The operator modifies the algorithm as it renders predictions, and this cycle is continued until the algorithm is accurate and performs to a high standard.
Classification, Regression, and forecasting all fall under supervised learning.
Classification
In classification tasks, a machine learning algorithm must deduce a result from existing data and determine which category new observations fit. For example, the software must assess current data and distinguish emails as “spam” or “not spam” accordingly.
Regression
The ML algorithm must evaluate and understand the correlations between the variables in regression problems. Regression analysis benefits forecasting and prediction by focusing on a single dependent variable and other changing factors.
Forecasting
A typical method for analyzing trends, forecasting generates predictions based on facts from the past and present.
Semi-Supervised Learning
In the first case, all observations in the dataset either have labels or none. Semi-supervised learning is the answer to this. Labeling usually requires hiring experienced professionals, making it a costly exercise. Semi-supervised methods are the most suitable choice when most observations are unlabeled but a few are labeled. These techniques take advantage of the fact that, even though the unlabeled data has unknown group memberships, it still holds essential information about the group parameters.
Unsupervised Learning
In this case, the machine learning algorithm inspects the data to detect trends. There’s no human operator or answer key to provide direction. Instead, the machine examines the data to uncover correlations and links. The machine learning algorithm is supplied with a large quantity of data in an unsupervised learning process and probes and responds to it as needed. To explain the data’s structure, the algorithm attempts to arrange it somehow. This may involve clustering the data or laying it out to make it seem more organized.
It eventually grows more adept at making judgments based on data as it evaluates more of it.
Fall under the category of unsupervised learning:
Clustering: It involves assembling groupings of related data (based on defined criteria). It may divide data into several categories and analyze each piece of data to look for trends.
Dimension reduction: When determining the precise information needed, dimension reduction decreases the number of factors considered.
Reinforcement Learning
The technique attempts to make decisions that maximize the reward or minimize the risk by utilizing observations gathered from its interaction with the environment. The agent, a reinforcement learning algorithm, iteratively learns from its environment. It gradually acquires knowledge from interacting with the surroundings until it has explored every possible state.
Machine learning, which incorporates reinforcement learning, is a part of artificial intelligence. It allows software agents and machines to autonomously determine the optimum course of action in a given situation to maximize output. A reinforcement signal, or straightforward reward feedback, is essential for the agent to learn its behavior.
Many kinds of algorithms are utilized to solve this problem. Reinforcement learning is a well-defined problem type, and all its solutions are classified as reinforcement learning algorithms. For this, an agent must select the optimal action for the problem based on his current condition. This process, when repeated, is referred to as a Markov Decision Process.
Reinforcement learning follows these phases to create intelligent programs (also known as agents):
- The agent observes the input state.
- The agent is forced to take action using the decision-making function.
- The environment rewards or reinforces the agent when the activity is completed.
- Information about the reward’s state-action pair is saved.
Some popular Machine Learning algorithms
Below is a list of commonly used machine learning algorithms. These algorithms are applied to almost any data problem:
Linear Regression
Using a continuous variable, we can estimate absolute values such as home costs, calls made, total sales, etc. We can identify the relationship between the independent and dependent variables by finding the optimal line. The linear equation Y=a*X+b illustrates this regression line, which best fits the data.
When solving for:
Reliving this childhood memory unlocks the secret to linear regression. As an illustration, if you asked a fifth-grader to rank their peers in ascending weight order without knowing their weights, they would likely assess their height and physical features before listing them. This is the same concept as linear regression! The young learner has realized that weight and size are related to the equation above.
- The dependent variable is Y.
- A – Slope
- X is an independent variable
- while b is the intercept.
These coefficients a and b arise from minimizing the sum of the squared distances between the data points and the regression line.
Logistic Regression
Logistic Regression is a supervised learning technique widely used to predict categorical variables or discrete values like Yes/No, 0/1, Red/Blue, etc. Machine learning often uses it to address classification problems.
Like linear regression, logistic regression solves classification problems and predicts discrete values. Its output is an S-shaped curve between 0 and 1, also known as the logistic function, which uses a threshold to determine whether the output is 0 or 1. Values over the threshold tend towards 1, while values below the threshold tend towards 0.
Decision Tree
People often choose this algorithm for its appeal. Supervised learning highly utilizes this strategy to address classification problems. Surprisingly, it works with continuous and categorical dependent variables. With this approach, we break the population into two or more similar groups based on the essential characteristics/independent variables to generate as many distinct groups as possible.
Playing the classic Microsoft game Jezzball is an excellent way to understand how a decision tree works (as shown in the image below). You must build walls in a room with moving walls to clear most of the area while avoiding the balls. Hence, you create partitions by constructing walls, separating the space into two distinct populations. In an unsupervised learning process, a large quantity of data is inputted into the machine learning algorithm, which then probes and responds accordingly.
Support Vector Machine algorithm
The algorithm primarily serves the purpose of categorization but can also handle classification and regression tasks through supervised learning. This technique attempts to construct a hyperplane or decision boundary that aggregates the data points and thus categorizes the datasets. ‘Support Vector Machine’ refers to the support vectors, i.e., the data points that help form the hyperplane. Its applications range from face identification, picture categorization, and drug development to other practical uses.
Naive Bayes
A Naive Bayes classifier is a classification method that uses the Bayes theorem, assuming that the predictors are independent. To explain it, it believes that one feature in a class has no bearing on the presence of any other. For example, a fruit is categorized as an apple if it is red, round, and 3 inches in diameter. A Naive Bayes classifier would consider each feature individually despite being interdependent or requiring additional attributes.
The Naive Bayesian model is simple to construct and especially beneficial for substantial data collection. It is also straightforward and performs better than the most complex classification techniques.
From P(c), P(x), and P(x|c), the posterior probability P(c|x) may be calculated using the Bayes theorem. For example, look at the following equation:
pc|x=P(x|c)P(c)P(x)
Pc|x=P(x1|c)*P(x2|c)*…………………..P(xn|c)*p(c)
Here,
- The posterior probability of a class (target) given a predictor is denoted by P(c|x) (attribute).
- The prior chance of the course is P(c).
- The likelihood, or P(x|c), is the probability of a predictor for a particular type.
- P(x) is the predictor’s prior probability.
K-Nearest Neighbour (KNN)
A supervised learning approach, K-Nearest Neighbor, can be applied to classification and regression tasks. This algorithm functions by comparing the similarities between new and existing data points and classifying the new data point into the category with the highest similarity. This method is sometimes called a ‘lazy learner’ as it holds onto all available datasets and uses K-neighbors to classify each new example. Any distance function can calculate the distance between the data points and assign the new case to the most similar class, with Euclidean, Minkowski, Manhattan, or Hamming distance being the most common.
K-Means
It is an unsupervised algorithmic approach that resolves the clustering problem. It uses a limited number of clusters to categorize a given dataset (assume k groups) straightforwardly. As a result, data points within a cluster are homogeneous to one another and heterogeneous to those in other clusters. K means clustering, which is a kind of inkblot test; the shape and distribution help to determine how many different clusters/populations there are.
K-means cluster formation:
- K-means selects k centroids, or points, for each cluster.
- With the nearest centroids or k clusters, each data point forms a group.
- Depending on the current group, members determine the centroid of each group. New centroids are present here.
- Repeat steps 2 and 3 as necessary to get more centroids. Get associated with new k-clusters and determine the distance that separates each data point from new centroids. Continue doing this until convergence, or the centroids stay the same.
Random Forest algorithm
Random Forest, a supervised learning technique, is suitable for classification and Regression in machine learning. We adopt an ensemble learning approach to improve the model’s accuracy, combining several classifiers to produce more accurate predictions. Increasing the number of decision trees (e.g., 64–128) within the random forest generally increases the model’s accuracy. The more trees, the higher the algorithm’s accuracy.
Each tree provides a classification result for a new dataset or item, and the algorithm forecasts the outcome based on the majority votes.
The random forest method quickly and effectively handles incomplete and inaccurate data.
Dimensionality Reduction algorithm
Data collection has grown exponentially in the past four to five years. Businesses, governments, and research centers are creating new data sources and collecting information precisely. For example, e-commerce companies gather customer data to offer more tailored service than a neighborhood grocery store. This data includes demographics, web browsing habits, preferences, past purchases, and comments.
As data scientists, we receive data that contains many attributes, thus offering an excellent opportunity to build a robust model. Whatever the issue, how do we determine the most significant variables in 1000 or 2000? Dimensionality reduction techniques like Decision Trees, Random Forests, PCA, Factor Analysis, Identity-based on the correlation matrix, and missing value ratio, among others, can assist us in such cases.
Apriori algorithm
The apriori algorithm, an unsupervised learning method, addresses association issues. It is designed to work on databases comprising transactions and construct association rules through frequent itemsets. These association rules assess how closely or distantly two items are related. This approach efficiently computes the itemset using a hash tree and a breadth-first search.
The program repeatedly searches the vast dataset to locate the frequent item sets.
In 1994, R. Agrawal and R. Srikant unveiled the apriori algorithm. It facilitates the identification of items likely to be bought together and is mainly used for market basket analysis. It can also be leveraged to recognize patient medication responses in the medical domain.
Final notes
Various criteria can be employed to classify different machine learning algorithms, but a learning task is an optimal approach to grasp the larger picture of ML. Depending on your issue and the data available, you should be able to decide whether to use supervised, unsupervised, or reinforcement learning. In future posts, we will provide additional instances of each type of machine-learning algorithm. Stay with us.