If you work in computer science, data science, or other related fields, you've probably heard the terms statistical learning and machine learning before. Contrary to what some people think, statistical learning (or statistical modelling) and machine learning aren't the same. While both fields of study are data-driven, there's a substantial difference between them.
In this article, we'll help you develop a better understanding of how both fields of study differ and put an end to the "statistical learning or machine learning" dilemma that data scientists debate about all the time. Check out the best machine learning courses, to begin with.
Hint: Statistical learning is actually one of the frameworks used in machine learning!
What Is the Difference Between Machine Learning vs Statistical Learning?
Statistical and machine learning are both subsets of artificial intelligence, the science of making machines that perform tasks in a smart way (similar to how humans execute tasks). Both are based on learning from big data, but they differ in the way that predictions are made.
What Is Machine Learning?
To put it simply, machine learning is the science of algorithm-based programming used to make a machine "learn" just as a human being would. And why is machine learning important?
Think about the Face ID facial recognition system on Apple's latest iPhone models, for example. The iPhone requires you to set up the feature by providing a 3D facial map of your face so you can unlock your phone by simply looking at the screen and make it impossible for anyone else to unlock it, even if they have a printed photo of your face.
But you probably don't look the same every day, do you? There are times when you'll be wearing your sunglasses or kicking a cool new haircut that completely changed how you look. How would your iPhone identify you? The answer is machine learning. Your phone won't be unlocked the first couple of times with your sunglasses on, so you'll have to input your pin code to open it.
Eventually, the iPhone will "learn" that this person who has their sunglasses on is actually you, so the next time you try to unlock it with your face, it'll work! Sounds awesome, right?
That's the power of machine learning. Machine learning allows computers to learn new things by observing how the user interacts with the system and making accurate predictions. Machine learning is not dependent on any form of programmed instructions. Machine learning allows a computer to learn from billions of observations.
What Is Statistical Learning?
On the other hand, statistical modelling theory is all about making predictions using rule-based explicitly programmed instructions and identifying relationships between variables. It's based on statistics, the mathematical science concerned with analyzing data and creating statistical models to make inferences about the population based on the data of an appropriate sample size.
The Differences Between Machine Learning and Statistical Learning
Statistical learning is often thought of as being a subcategory of machine learning. Typically, the learning process in machine learning goes as follows:
- Making observations of the phenomenon
- Creating a model that represents this phenomenon
- Making predictions based on this model
So, how do things differ in statistical modelling? Well, for starters, the learning process must be automated to allow the machine to absorb it and construct a statistical model.
The statistical model's information from observing the phenomenon will also be used to make predictions about any past or future events related to this phenomenon. It'll also be used in establishing relationships between variables and whether a specific relationship between variables has more weight than the other.
Statistical modelling doesn't require a vast number of observations for inference; a small dataset representing a sample of the population with a few attributes would suffice to make a hypothesis.
A real-world example of a statistical model is predicting the price of a stock 6 months from now based on its historical prices and establishing the relationship between all the variables that may have a direct or indirect influence on its price. Another example from the medical field is identifying the risk factors of certain diseases that don't have a confirmed cause.
Another significant difference between machine learning and statistical modelling is that machine learning is fact-based, while statistical modelling generates inference based on assumptions, like normality and homoscedasticity. The statistical models are built based on these assumptions that are either validated or rejected after the model is created.
For instance, in linear regression, using the mean squared error can minimize the error between different data sets to get a function representing the data as much as possible. The same concept of error minimization in linear regression is used when building a statistical model.
Machine Learning Methods
Generally speaking, there are 5 categories of machine learning, which are:
Supervised Learning
With supervised learning, the machine learns with direct human intervention for guidance, where input data is regularly needed for the machine to be "smarter". The machine maps the input data and uses it to understand other similar data.
Supervised learning works in a similar way to that of when a teacher explains a particular subject to a student. The teacher teaches the student the fundamentals by showing them some examples. Then, in the exam, the teacher includes somewhat different questions to the examples that the student learned in the classroom.
If the student manages to get a passing grade, they've successfully learned the subject and can solve most problems related to it. The input here is the teacher's lessons and examples, while the output is the student's ability to "learn" and pass the exam.
A real-world example of supervised machine learning is image classification. The machine's goal is to learn how to predict an image's class and label it based on the maps it created using input sample images.
Unsupervised Learning
On the other hand, unsupervised learning is where the machine learns independently without input data from humans. It's similar to how you could try teaching yourself how to play a particular computer game entirely on your own by skipping the tutorials at the beginning.
A common application of unsupervised in marketing is customer segmentation, which is based on understanding customers' behaviour and preferences to create well-targeted marketing campaigns.
Semi-Supervised Learning
As its name implies, semi-supervised learning combines supervised and unsupervised machine learning by giving the machine a group of labelled, unlabeled, and partially labelled data to train on.
One of the semi-supervised applications is text document classification, where the machine classifies a small number of labelled documents and a large number of unlabeled documents.
Transfer Learning
Transfer learning is re-using a model in solving a problem that's somewhat related to the problem it's trained on. Instead of rebuilding a model and training it all over again to solve a particular issue, a trained model that's already capable of solving somewhat similar problems is used.
The advantages of transfer learning include saving the resources required to build a new model from scratch and not needing large data sets.
Reinforcement Learning
Reinforcement learning is exposing the machine to an environment and allowing it to maximize the learning potentials cumulatively. The applications of reinforcement learning are pretty limited in the time being, but its usage is gradually increasing. A beneficial application of this type is learning automatic parking policies.
Is Deep Learning Statistical Learning?
Yes! Deep learning is a statistical modelling method based on the extraction of attributes or features from raw data using the foundational concepts of statistics. Deep learning is concerned with creating algorithms using artificial neural networks that closely resemble the human brain's functions.
Traditionally, algorithms would reach a performance plateau that makes any further improvements impossible. Deep learning has paved the way for supervised learning to develop and improve algorithms continuously.
Implementing deep learning in a computer allows it to learn the same way humans do: by example. Some of this method's real-world applications include self-driving cars, visual recognition, natural language processing, and virtual assistants like Siri and Alexa.
Final Thoughts
Still, confused about the difference between statistical and machine learning? Well, let's recap our article.
Both statistical and machine learning are big data analysis methods that make our lives easier by creating accurate predictions and inferences. The core difference is that machine learning has a predictive power that can be made better with supervised or unsupervised learning approaches.
In contrast, statistical learning allows the machine to learn by providing it with an automated algorithm that it can use to create a hypothesis and make predictions based on calculated assumptions.
Each of the two approaches has different applications based on the machine's computing power and the accuracy needed in each particular application.
If you're considering a career in machine learning or data science, then understanding the distinction between statistical and machine learning will make you stand out from the crowd and give you an edge in no time.
