When it comes to the comparison of statistics vs. Machine Learning applications, there are two primary schools of thought. The first is that Machine Learning (ML) is just ‘glorified statistics’. The second is that ML and statistics are ultimately very different. Let’s jump in and explore.
While the two share common paths to get to an intended result, their goals are generally quite different. Machine Learning (ML) is a field of Computer Science and AI (Artificial Intelligence), while statistics is a subset of mathematics.
Statistics allows researchers to address scientific questions related to the causal impact of a certain variable on an outcome of interest. Analysts use statistics to evaluate, for instance, the effect of a redistributive policy on the distribution of wealth across the population of a country. Whereas companies may use Machine Learning to categorize customers into different segments based on free-form text feedback that they receive from them.
There are also tasks that can be achieved using either or. Here is an example:
When banks analyze the creditworthiness of clients, they need to look at multiple variables and then ‘summarise’ the information in such a way that allows them to distinguish between those who are ‘creditworthy‘ and those who are ‘not creditworthy’. This task can be addressed by setting up a statistical model or by training a Machine Learning model. The latter will probably be more accurate and powerful, but the first will be more interpretable and will allow banks to know why a certain person was classified, for example, as not creditworthy.
Statistics and Machine Learning are not the same
Speaking broadly, Machine Learning is a very powerful and revolutionary tool but it needs to be applied to very specific problems. Statistics is a discipline that is generally applicable to any evidence-based question grounded on some hypotheses.
Statistics and Machine Learning both follow a common path. They can even be used in similar ways, but they tend to have different results.
Statistics have long been used to analyze data and make inferences. Results are driven through probability models that vary by project. These models are commonly made up of three components: the sample space, the family of events, and the probability measure. With probability models, predictions for an outcome are made while measuring confidence in the said prediction.
In contrast, Machine Learning relies on learning algorithms to assess patterns in data and make predictions. ML is ideal for ‘wide data’ or ‘unstacked’ data that has more input variables than it does subjects. This is in comparison to ‘long data’ that has more subjects than input variables. ML algorithms are ideal for less controlled experiments because they make fewer assumptions. This also makes it optimal for non-linear data that doesn’t depict a clear-cut relationship.
Statistics vs Machine Learning
Any modern-day data scientist or ML engineer has considered whether the concepts of Machine Learning vs statistics can be used interchangeably. While statistics have been around for several centuries, Machine Learning is now gaining popularity, despite having been developed within the last 75 years.
The differences between the two mean that Machine Learning and statistics cannot and should not be used for every task interchangeably. It’s important to differentiate between them, so what are the differences?
1. Uncertainty tolerance
Statistical modeling has a low uncertainty tolerance. It requires a lot of attention to be paid to uncertainty estimates like confidence intervals and hypothesis tests.
Scientists commonly use the ‘true value’ methodology to predict that the correct value lies within a series of predictions. For example, a measurement of 4.11g ± 0.3 means the true value could be anywhere from 3.81 to 4.41g.
On the contrary, Machine Learning modeling tolerance is much higher than statistics because there are little to no assumptions being made. Furthermore, Machine Learning algorithms offer higher plasticity because their requirements are far less rigid than statistical models.
2. Data requirements
Statistical models struggle with large datasets and become less reliable as they reach a certain threshold. On average, attributes are limited to 10-12 because they are likely to begin overfitting as attributes grow. Overfitting is when a statistical model fits far too closely with its training data and begins producing inaccurate predictions.
One distinct difference between the applications of statistics and Machine Learning is that the vast majority of statistical models follow parametric methods. This means they are based on a fixed number of parameters and make assumptions based on those parameters.
Machine Learning models are more of a non-parametric (also known as ‘distribution free’) approach that does not make assumptions about the distribution of a set of data (for example, normal distribution).
Some may see the non-parametric approach as a disadvantage of Machine Learning vs statistics because parametric is generally ideal as far as better accuracy goes.
When to use statistics vs Machine Learning
In terms of statistics vs Machine Learning, the latter wouldn’t exist without the former. However, it is safe to say Machine Learning is pretty useful in modern-day businesses as nowadays the amount of data we have access to is usually very large.
Comparing Machine Learning and statistical models can be difficult. Which you use depends largely on what your purpose is. If you just want to create an algorithm that can make predictions on topics such as the performance of an ad or real estate pricing, Machine Learning is probably the best pick. If you are trying to prove a relationship between variables or make inferences from data, a statistical model is perhaps the better approach.
When determining whether statistics or Machine Learning models better fit your needs, it ultimately depends on your use case. There isn’t a one-fits-all approach to this question, but take a look below for some potential answers according to what your intended use case is.
All in all, to make the most informed decision on your next data-driven project, be sure to carefully consider the advantages and disadvantages of both ML and statistics.
Machine Learning use cases
The general population is mostly familiar with methods related to traditional statistics. Some of us may have even had a class that specifically dealt with statistics in high school or in a post-secondary institution.
This blog post, however, aims to help further educate on ways to optimize and improve processes through Machine Learning. Some real-world examples of Machine Learning include Sentiment Analysis, image analysis, and document categorization.
Sentiment Analysis and NLP (Natural Language Processing)
This method is ideal for prioritizing support tickets based on sentiment. For example, you might organize tickets by positive, neutral, and negative feedback and act accordingly. Another common use is brand monitoring. Sentiment Analysis helps brands quickly identify and react to negative feedback.
Machine Learning can produce CV (Computer Vision) algorithms to process images much like how human eyes do. This is helpful because the algorithms take images and transform them into meaningful data. Then, actions or recommendations are made based on the findings.
In this Real Estate example, images of rooms are analyzed and classified according to pre-determined labels such as Bedroom, Living room, and Kitchen.
The advantages of Machine Learning include document classification, which applies specific tags to documents based on their content. Document classification can be a long, manual process, or it can be automated using ML.
In the above example, ML capabilities are utilized to automatically analyze and categorize incoming email attachments.
In addition to being a faster alternative, automatic classification methods through Machine Learning algorithms provide less error than a human would because they do not get tired, overworked, or bored!
Machine Learning vs statistics – what are the key takeaways?
While there’s some overlap between Machine Learning vs. traditional statistics, the two do carry key differences. Because of this, they should not be used as interchangeable terms.
One primary difference in statistics vs. Machine Learning applications is that Machine Learning provides a level of interpretability that is not possible with statistics, which also means that scientific problems, in general, cannot be solved with Machine Learning algorithms.
Realistically, it is uncommon for most people and businesses to need to solve scientific problems. Rather, innovation and automation take precedence, which is why Machine Learning is commonly the best fit.