Machine learning conjures up ideas of robotics, artificial intelligence, and flying automobiles, whereas statistics conjures up charts with bell curves and sports game outcomes. However, these two areas have a lot of overlap since they deal with data analysis. Statistical modeling and machine learning can even be used in similar scenarios to answer several queries.
Looking deeper at what we consider machine learning versus statistics raises several issues. Knowing where machine learning picks up statistics leaves off may be advantageous for people interested in developing their data careers.
What is machine learning?
Artificial learning is a subset of Machine Learning. It is the process through which computers use enormous volumes of data to detect patterns and make judgments without human interaction. Text mining and sentiment analysis are two examples of how Machine Learning is employed.
Machine learning may be classified into supervised, unsupervised, and reinforcement learning. There is a goal result variable in supervised learning. There is no aim in unsupervised learning, and the computer merely finds patterns and correlations in data. Reinforcement learning is a procedure that incorporates an algorithm that uses trial and error to achieve a goal.
While not centuries old, machine learning is not new and has been widely investigated since the 1950s. Over the last two decades, it has grown in popularity due to the exponential development in data collecting and improved processing capacity.
What is statistical learning?
Whereas machine learning is a vast topic that involves how computers can interpret and “learn” from data, statistical learning focuses on transforming raw data into usable information and serves as the foundation for machine learning algorithms.
The two topics are inextricably linked because statistical learning may construct the basic models that control how a machine learning algorithm perceives data. A simple example of this in action is a linear regression algorithm, which is a form of machine learning algorithm based on statistical concepts.
Statistics knowledge is essential for debugging faults with machine learning algorithms and tackling broader data analytics concerns. Consider a machine learning system that is very accurate in a test environment but gets less accurate when applied to real-world data. Statistics knowledge assists professionals in understanding why and how to address the underlying problem. Statistics expertise also opens the door to many data occupations, from marketing analysis to data science.
How are machine learning and statistical learning related?
Many machine learning approaches are derived from statistics (for example, linear regression and logistic regression) and other fields such as calculus, linear algebra, and computer science. However, this relationship with underlying statistical procedures leads to many individuals conflating fields.
Surprisingly, younger machine learning engineers and data scientists who utilize Python machine learning programmes like sci-kit-learn may be ignorant of the underlying link between machine learning and statistics.
This separation of machine learning from statistics through the use of libraries is frequently cited as one of the reasons why some people argue that knowledge of statistics is not required to do machine learning. While this is true for more fundamental jobs, skilled data scientists and machine learning engineers create models using their understanding of probability and statistics.
Machine learning vs statistics in the real world
Machine learning has various applications in many sectors, but what constitutes a successful machine learning challenge is a question of scale. Because machine learning algorithms learn from data, they may be employed more successfully when a significant amount of data is available. For example, researchers can examine the behavior of computer programmes to discover potential malware instances; however, researchers have access to billions of data points from sources such as event logs and other security analysis tools. Manually analyzing this data would take decades, but machine learning can substantially reduce the time required to process this data and derive valuable conclusions.
Critical Differences between Statistics and Machine learning
System learning is a subset of artificial intelligence domains in which the machine trains itself and predicts the consequences. Machine learning is essentially the use of algorithms to prepare data. Most data analysts regard it as a black box at times. You teach the machine (computer or model) your set of rules (data points). Statistics is an area of mathematics in which patterns in data are discovered using mathematical solutions. Statistics is a branch of mathematics. Some geometrical patterns might be detected to extract insights or connections between the data, obtained using mathematical procedures (statistics). Statistics have a role in identifying the trend.
You provide the machine with some conditional logic in basic terms or notations. If X1 and X2 are both equal, then Y=estimator. Similarly, many data points are merged to produce the estimate or prediction. This is something that the machine accomplishes on its own. It learns with all of the data put into it, and when new values are provided, it automatically provides the estimate.
It is critical to comprehend the data and detect any connections and trends before giving it to the computer. If there is a correlation between two or more data points, it is highly relevant in making the correct forecast.
Most firms in artificial intelligence are currently focusing on automation and robots. Statistics, linear algebra, probability, and geometry serve as the foundations for such fields. Data insight or any data-related problem may be solved using mathematics.
A statistician’s skill set includes machine learning and statistics, including descriptive statistics or statistical modeling. Whereas machine learning is concerned with hypotheses, categorization, and data structures, it also necessitates a fundamental understanding of programming and algorithms.
Statistics and machine learning are sometimes grouped because they employ comparable methods to achieve a goal. However, the aims they are attempting to achieve are vastly different. The purpose of statistics is to conclude a population based on a sample. Machine learning is used to detect patterns in data to produce repeated predictions.
To create good predictions, machine learning needs a significant amount of data. Models are created with training data, fine-tuned with a validation dataset, and tested with a test dataset. All of these stages assist the machine in “learning.”
Because you are not attempting to forecast, statistics do not include various data groups. In this case, the goal of modeling is to show the link between the data and the result variable. Furthermore, significance tests are used in statistics to identify the direction and degree of a connection while accounting for noise and confounding factors.
Because of the high number of variables in machine learning datasets, the models generated from them can be incredibly accurate while also being nearly hard to explain. On the other hand, statistical models are often easier to grasp since they are based on fewer variables, and statistical significance tests confirm the correctness of correlations.
Statistical analysis and social media
Websites like Facebook and other social media platforms employ statistical modeling to study information acquired from users about demographics, engagement and reach to understand better how people connect on their platforms. This information may be utilized to anticipate human behavior based on user-generated data in certain circumstances.
Understanding what a specific collection of activities entails in terms of a person’s anticipated political beliefs, economic position, or even age range helps platforms to more precisely target their adverts and services, assisting them to increase income and extend their user base.
Furthermore, machine learning and analytics are rapidly being used for customer support tasks connected to these platforms. Chatbots and machine learning systems are programmed to react to the most frequent user complaints and enquiries, allowing businesses to focus their customer support personnel on complicated or highly escalated cases. As a result, they may maintain a quick response time to customer contacts while ensuring that high-level requests are provided with the degree of information that would keep consumers pleased with the response.
Statistical Modelling and Software Development
Detailed data based on bug reports may be used to understand how programmes and platforms change in response to their user base over time. Products like Debian-based operating systems are created to be free and open to the public, with the public obtaining a product and the developer receiving significant data inputs in return.
While this method generates a significant quantity of data, the very varied nature of the reports and the risk of erroneous reports due to human mistakes make it unsuitable for machine learning.
This does, however, lend itself to the statistical examination of faults as they relate to the essential operation of the programmes. As a result of the study’s findings on frequently unstable parts of the software, developers may focus their efforts and solve the most prevalent and severe flaws.
In the end, the distinction between statistics and machine learning is that machine learning encompasses the convergence of a variety of techniques and technologies, which may include statistics and statistical modeling. In contrast, statistics focuses on using data to make predictions and create models for analysis.
While statistics are crucial for machine learning to construct more advanced algorithms, not every problem is a machine learning problem. For example, machine learning can assist in automating data analysis. However, not all data sets are large enough to support automation—in this situation, statistics can still be used to find patterns and extract useful information without machine learning.
Is one more valuable than the other?
No. You can’t assign a value to two disciplines that perform distinct functions. Your objective or industry will influence whether you construct a machine learning model or a statistics model.
As previously stated, interpretability is a crucial issue to consider. A data scientist may not need to defend the decisions of a complex model that recommends how many widgets to produce. A model intended to make more sensitive judgments (for example, issuing or rejecting a loan) may not be appropriate if a data scientist cannot demonstrate the strength of correlations between predictors and outcome variables.
Where do AI Experts and Data Scientists fit into one picture?
While we’ve previously addressed both words in this article, we believe it’s crucial to clarify how data science and artificial intelligence (AI) come into the statistics/machine learning argument.
Artificial intelligence is an area of computer science that focuses on creating machines that can accomplish people’s activities. This field includes machine learning as a subset.
Data science is an interdisciplinary discipline that combines computer science, arithmetic, statistics, and machine learning to gain insights from massive data sets. Data scientists use large volumes of data generated by businesses and governments to solve issues or identify possibilities. They frequently use their statistical knowledge to assist them in constructing models that are best suited for the task.
Statistics and machine learning are inextricably connected. Like comparing a square to a rectangle, machine learning usually depends on statistics, but statistics are not necessarily machine learning. Combining these technologies in their most basic versions can yield in-depth insights from large data sets. The machine learning or statistical model, on the other hand, is only as good as the practitioner who uses it. It is critical to remember that a thorough comprehension of and familiarity with the data is essential for selecting the best tools for the job.
Also Read: The Classic Machine Learning Workflow