We live in quite an interesting time as we see technologies around us developing at a breakneck speed. We are also seeing data overtaking crude oil to be the most valuable resource available for this generation’s businesses. So, presenting to you the Types of Machine Learning Algorithms.
The transition of computing power from traditional on-premise mainframe data centres to easy-to-use and ever-scalable cloud computing has unlocked limitless possibilities to make use of data like never before. And we are just getting started to harness the true power of data.
Data Science is probably the most famous buzzword in the domain of technology and IT right now. We are seeing the democratization of several tools and techniques working in tandem with the boost in computing.
Using the data on almost everything, we can enable computers to learn and replicate actions like humans do. We can also improve their learning speed in an autonomous manner by feeding them with data and information over time in the form of real-world observations. This is what is known as machine learning.
- Machine learning is the process of computers learning and replicating human actions through data and the application of algorithms based on real-life observations.
- There are 3 categories of Machine Learning Algorithms – Supervised Learning, Reinforcement Learning and Unsupervised Learning.
- The decision Tree Algorithm is mostly used in classification problems for machine learning applications. It also works for both types of categorical and continuous dependent variables.
- Linear Regression Algorithm is very famous for generating real value estimates as per one or more independent factors as per the application that you want to use it for.
What are the categories of Machine Learning Algorithms?
Supervised Learning algorithm includes a target variable that needs to be predicted from a set of predictor variables. By utilizing these predictor variables, the generation of a function mapping from inputs to desired outputs is done.
The training process is done until the model achieves a pre-set level of accuracy on the training data. Some of the examples of supervised learning are Decision Tree, Random Forest, KNN, Regression, Logistic Regression, etc.
Reinforcement Learning algorithm enables the machine to make specific decisions after it is trained. Using these algorithms, the machine trains itself using trial and error processes after continually interacting with the environment.
Machine learning happens through past testing and its output. It takes into account the process which is most efficient to reach the desired output. This process is further perfected by the machine over several iterations of trial and error cycles. One example of Reinforcement Learning is the Markov Decision Process.
In the case of Unsupervised Learning, there is no target variable or any desired outcome to achieve. It is mostly used to cluster up populations in various groups. These clustered groups are extensively used for market segmentation into different groups. Some examples of Unsupervised Learning are K-means and Apriori algorithms.
Types of Machine Learning Algorithms
This is a supervised learning algorithm that finds a lot of applications in classification problems. It is also one of the few algorithms which work in both cases of categorical and continuous dependent variables.
This algorithm works by splitting the population into two or more homogeneous groups. This happens through more independent variables to make it as unique as groups as possible.
This algorithm is used to generate real value estimates depending on the application that you want to use it for. This is achieved by using continuous independent variables to predict dependent variables. A relationship is established between independent and dependent variables through a linear equation in the form of Y=aX + b.
In the above equation, Y is a dependent variable and X is an independent variable. The values a and b are slope and intercept respectively. These coefficients are derived from the minimization of the sum of the square difference of distance between data points and regression line.
To understand the entire concept of linear regression, we can take an example. We can take a person and can ask them to arrange their fellow employees by increasing order of their weight. That person can just visually analyze various factors like build type and height for each of his/her fellow employees. Based on these variables when taken into consideration in a combined fashion will provide a good estimate of everyone’s weight.
A logistic regression algorithm is used to predict discrete values on the basis of a given set of independent variables. By discrete values, it means either true or false or 0 or 1. To put things simply, this regression algorithm estimates the probability of occurrence of a particular event by fitting data to a logit function.
This is the reason why it is also known as logit regression. As this algorithm estimates probability, its output value is in the form of either a 0 or a 1.
Let’s break down this algorithm with an example.
Imagine that you have to solve a puzzle. Now, there are only two outcomes to this event- either you complete the puzzle or you don’t.
But if you’re given a variety of puzzles to get an idea as to what subjects you are good at, it will have a different result. The outcome of this experiment will be something like this – if you are given an analytical puzzle, you are 80% likely to solve it. But if you are given a language-based puzzle, you are 40% likely to solve it. This is what Logistic Regression does.
It is a classification algorithm based on Bayes’ Theorem with independence between predictor variables. A Naive Bayes classifier presumes that the involvement of a particular feature in a class is inconsequential to the presence of any other feature.
For instance, an object is considered to be a diamond if it is shiny, reflects light, and has a distinctive sheen to it. Now, even if the reason behind these parameters might be dependent on each other, a Naive Bayes classifier would take into account all of these attributes to independently contribute toward the probability that the object is actually a diamond.
This algorithm is easy to apply and is especially effective for very large data sets. But make no mistake about its efficacy as it outperformed even the most sophisticated classification algorithms.
Support Vector Machine
This is also a classification algorithm. This algorithm uses plots of each data point as a point in n-dimensional space with the value of every attribute being the value of a particular coordinate. In the n-dimensional space, the n signifies the number of features you are considering.
To put things simply, the objective of this algorithm is to find a hyperplane in an n-dimensional space that uniquely classifies the data points. In order to separate the two classes of data points, various possible hyperplanes are present and can be chosen. But the objective is to find a plane that has a maximum margin.
Here, the margin is defined to be the maximum distance between data points of both of the classes. By maximizing the margin distance, it will impart reinforcement for future data points to be classified with a higher level of confidence.
This is a type of unsupervised learning algorithm. It solves the clustering of data points problem. The algorithm provides a simple way to classify a provided data set through a set number of clusters. This set number of clusters is assumed to be k number of clusters.
All the data points inside a cluster are homogeneous in nature. But the clusters themselves are heterogeneous, thus forming distinct groups. The following diagram provides how the clusters and their groupings look like
K-means clusters are formed through a procedure. First, the K-means algorithm takes k number of centroids for each cluster. Each data point forms a cluster around the closest centroids. It then finds the centroid of each cluster as per their existing cluster members.
This results in the creation of new centroids for each cluster. The process repeats itself as it finds the closest distance of each data point from new centroids and is then associated with the newly formed k-clusters. This process stops after full convergence occurs where no new centroids come up.
Random Forest algorithm is basically a collection of decision trees and is an ensemble learning method for classification, regression, and various other tasks. A collection of decision trees or “forest” is taken into consideration to implement this algorithm.
The Random Forest algorithm works through each individual tree in the forest giving out a class prediction and the class with maximum votes becomes the model’s prediction. The reason why Random Forest works really efficiently is that it utilizes a large number of uncorrelated trees working as a committee.
The low correlation between models is the crucial element of the entire algorithm. Uncorrelated trees can come up with ensemble estimates that are more accurate than their individual estimates.
The reason behind this is that these trees protect each other from their individual inaccuracies/errors. But it doesn’t get rid of the fact that there will be few trees with wrong predictions pointing in the wrong direction. As long as the group of trees moves in the correct direction, Random Forest would work just fine.
It takes the concept of “birds of the same feather flock together” and provides a really powerful application through this algorithm.
k-Nearest Neighbors or kNN algorithm works on the assumption that similar things belong together. Or more precisely put, they exist in close proximity. In the image given below, you can see that similar data points are grouped together.
The grouping is done based on the similarity (which is popularly determined by the Euclidean distance) between data points. But there are many other ways to calculate the straight line distance between points on a graph.
The advantage of the kNN algorithm is that it is quite simple and convenient to implement over a large number of use-cases. It also removes the cumbersome task of building a model and managing numerous parameters.
There is also no need to provide any assumptions, which is why the kNN algorithm is versatile. It can be used for search, classification, and regression. But the algorithm becomes notably slower when it is loaded with a higher number of predictor variables.
Principal Component Analysis (PCA)
PCA is a machine learning algorithm that uses an orthogonal transformation to convert a set of data points of possibly correlated variables into a set of values of linearly uncorrelated variables labelled as principal components.
Some of the use-cases of PCA are found in compression, simplification of data for easier comprehension, and visualization. Although one needs to have enough domain knowledge before moving forward with PCA as their preferred machine learning in their use-case.
Independent Component Analysis (ICA)
Independent Component Analysis (ICA) is a machine learning technique to distinguish and separate sources from a mixed-signal. Independent Component Analysis works on the principle of independent components, instead of principal component analysis. It is a statistical method for unearthing hidden factors amongst sets of random variables or signals.
This algorithm defines a generative model for the observed multivariate data, which is typically given as a large collection of datasets. In the model, the signals are assumed to be linear combinations of some unknown latent variables. The combination system is unknown too.
The latent variables are assumed to be non-Gaussian and mutually independent. These are called independent components of the observed data. And these independent variables are referred to as factors that are found out using ICA.
I hope this article gave you a fair insight into the different types of machine learning algorithms commonly used. Different types of machine learning algorithms can be used to solve different sorts of problems. The decision should be made based on the requirements and what fits the best for you.
The application of machine learning in our daily lives and in our businesses has been impactful. If you are interested or already working with machine learning, you should consider exploring various ways how machine learning can benefit a business’s needs. You can even start off your own business based around machine learning too if you’re really adept at it. But it is advised to start off with smaller, model projects before trying things on a bigger scale.
Frequently Asked Questions (FAQs)
What are the machine learning applications?
There are various machine learning applications like image recognition, face detection, speech recognition, traffic prediction, etc.
Why is machine learning used?
Machine learning offers the user to feed a computer with an immense amount of data and to let computers analyze to come up with data-driven insights. This takes the load off humans to make sense of data at an extremely huge scale.
Is machine learning hard to understand?
If you are creative, like to experiment and have a tenacity towards data both as a science and an art, then machine learning won’t be that hard to learn and work with. But, being good with maths and statistics is a huge plus in this domain.
What is the most important aspect of machine learning?
Training the model is the most important aspect of machine learning. It requires choosing the features and hyper parameters really carefully. Data cleaning also becomes a large part of data that is fed for machine learning too.
How fast can you learn machine learning?
Machine learning courses, especially self-learning, will require around 6-18 months to fully learn at a moderate level. But the duration of the learning would solely depend on the curriculum that the course you have opted for.