“Machine Learning (ML)” and “Traditional Statistics(TS)” have unique philosophies in their approaches. With “Data Science” in the forefront getting masses of attention and interest, I want to dedicate this blog to discuss the differentiation among the 2. I frequently see discussions and arguments among statisticians and data miners/machine learning practitioners at the definition of “data science” and its coverage and the specified skill sets. All that is needed is simply being attentive to the evolution of these fields.
There is no doubt that once we communicated about “Analytics,” each data mining/machine learning and conventional statistician had been a player. However, there may be a massive distinction in approach, applications, and philosophies of the 2 camps that is frequently overlooked.
Machine Learning
Machine learning is the future technology. It is developing at a rapid pace. During the previous couple of years, machine learning has reached the next level. It is utilized in numerous fields like fraud detection, web search results, real-time ads on web pages and mobile devices, image recognition, robotics, and lots of different areas.
Machine learning is part of computer science. It has been advanced from the study of computational learning and theory in artificial intelligence. Machine Learning works with AI. In different words, machine learning offers the ability to the computers to research new matters with the help of some programs.
Machine learning is likewise beneficial to make predictions on data. It constructs a few algorithms that are operated via means of a model creation, and it is used to create data-driven predictions. Machine learning has performed a crucial position in the functionality of human society.
Statistics
Statistics is all about the study of collection, analysis, interpretation, presentation, and organization of data. Whenever we use statistics in scientific, and industrial problems, we start the system by deciding on a statistical model system.
Statistics perform a vital role in human activity. It means that with the help of statistics, we can track human activities. It facilitates us in identifying the per capita income of the country, the employment rate, and much more. In different words, statistics assist us to conclude from the facts we have collected.
When would you use machine learning over statistics?
Clough’s egg analogy is beneficial in exploring this further. “The machine learning model doesn’t tell you anything about running a more efficient farm,” he suggests, “while the statistical model would be unwieldy if you owned tens of thousands of chickens.”
When looking at massive data sets, machine learning can be extra optimal. Many concede statistics are not able to provide a deeper analysis at the relationships and correlations among the data whilst the stages of data are high. They additionally can’t be relied on for causation, probability, and certainty because the chance is that they are probably misinterpreted or intentionally misused to back up a selected argument. As the well-known announcement goes: “There are lies, damn lies, and there are statistics.” Human involvement provides a weakness in statistics.
Basios, however, explains you could observe statistical modeling while you understand “specific interaction effects between variables” and “have prior knowledge about their relationships”. Machine learning, meanwhile, may be used when aiming for “high predictive accuracy”.
It has a tendency to be ‘deployed’ extra on the source of the data; as that is accrued and grows, algorithms robotically start to provide intelligence. In manufacturing, for instance, it may predict whilst machines will need maintenance, reducing disruption. It also can examine one option when compared to another – predicting which outcome would be better.
Industries Using Machine Learning
The evolution of computers and technologies has produced machine learning. Machine learning has modified the manner we live our lives. There are masses of industries that are using machine learning.
- Google is using machine learning in their self-driven cars. Netflix is one of the most tremendous examples of machine learning technologies. Netflix is using machine learning to customize the content for its customers.
- It analyzes human behavior and then presents the best-matched content to the customer. Machine learning is likewise beneficial in fraud detection, and it facilitates the brands to be secure in nearly each platform.
- Machine learning is getting more popular due to the fact the data is also developing at a rapid pace. It permits us to analyze a big amount of data in much less time and low cost with the assistance of powerful data analysis methods. It facilitates us to quickly produce models that can examine the massive amount of data and deliver faster solutions, even on a huge scale.
Industries Using Statistics
Almost every industry uses statistics. Because without statistics, we can’t get the conclusion from the data. Nowadays, statistics is important for diverse fields like eCommerce, trade, psychology, chemistry, and much more.
Business
Statistics is one of the tremendous aspects of companies. It is playing an important position in the industry. Nowadays, the arena is becoming more competitive than ever before.
It is turning into more difficult for the business to live in the competition. They want to fulfill the customer’s goals and expectations. It can only happen if the organization takes quick and better decisions.
So how can they do so? Statistics play an important position in understanding the goals and expectancies of the customers. It is, therefore, crucial that brands take quick decisions so that they can make better decisions. Statistics provide beneficial insights to make smarter decisions.
Economics
Statistics is the base of Economics. It is playing a crucial function in economics. National income report is a vital indicator for economists. There are diverse statistics strategies carried out on the data to investigate it.
Statistics is likewise useful in defining the connection among demand and supply. It is likewise required in nearly each aspect of economics.
Banking
Statistics play a vital element in the banking sector. Banks require statistics for a wide variety of different reasons. The banks work on pure phenomena. Someone deposits their cash in the bank.
Then the banker estimates that the depositor will now no longer withdraw their cash in the course of a period. They additionally use statistics to make investments the cash of the depositor into the funds. It facilitates the banks to make their profit.
State Management
Statistics is a vital factor of the development of the country. Statistical data is broadly used to make administrative level decisions. Statistics are important for the government to perform its duties efficiently.
Difference between Machine Learning & Statistics
| Machine Learning (ML) | Traditional Statistics (TS) |
|---|---|
| Goal: “learning” from data of all sorts | Goal: Analyzing and summarizing statistics |
| No rigid pre-assumptions about the hassle and data distributions in general | Tight assumptions about the hassle and data distributions |
| More liberal in the strategies and approaches | Conservative in strategies and approaches |
| Generalization is pursued empirically through training, validation and check datasets | Generalization is pursued the usage of statistical tests on the training dataset |
| Not shy of using heuristics in methods looking for a “desirable solution” | Using tight preliminary assumptions about data and the problem, generally looking for an optimal solution under those assumptions |
| Redundancy in features (variables) is okay, and often helpful. Preferable to use algorithms designed to handle large range of features | Often calls for independent features. Preferable to use less range of input features |
| Does now no longer promote data reduction prior to learning. Promotes a tradition of abundance: “the more data, the better” | Promotes data reduction as much as feasible before modeling (sampling, less inputs, …) |
| Has been confronted with fixing greater complicated problems in learning, reasoning, perception, knowledge presentation, … | Mainly targeted on conventional data analysis |
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