Statistics vs machine learning is most asked query by the students. In this technological world, where all the work is being done by computers. It is a masterpiece that is possible due to Humans. Over time, Humans have made possible taking Artificial Intelligence to a next level. Many Data Scientists have predicted that in the coming future, we as humans will be working according to computer orders. It is quite obvious that computers are an invention made by humans. The programming level is so quick that it learns the language that is made by Humans in no time.
The working of a machine is dependent on the data. Without data you cannot program any language, command, or codes for the computer. The database is the key factor that helps in the making of a program. With machine learning what comes in handy is the statistics. They say that Machine learning and statistics are hand-in-hand tools, which helps in programming a software or a language.
There are ample other tools that are helpful for programming a language, a command, or even coding. But the most important or we can say a crucial element is the statistics. That helps in the inference of Data and Machine Learning. That also helps in the prediction of the given data in a program.
In this article, we will discuss how statistics vs machine learning come hand in hand and are not the same thing. which most of the data scientists who are a beginner get confused with. Let’s see some of the most important differences between Statistics vs Machine Learning right away.
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Some key difference between Statistics vs Machine Learning
MACHINE LEARNING | STATISTICS |
| It deals with the prediction of the data from a program, code, or language. The origin of Machine Learning is somewhere connected with Artificial Intelligence. In this process, there is no need for human interference. During this process, it has already been taught to respond with programs. That has been already tested and performed previously to get the best prediction of the given Data. | It deals with the inference of the data from a program. The origin of Statistics is connected with the mathematics problem in a program. In this process, human interference is a must. As the variables get changed every time so it’s better to deal with it with a human hand to solve a mathematics dataset. |
| Machine Learning is used to analyze the existing material of data in a program. And it is also used to predict the future events of that particular data that has been examined by the machines. | Statistics is used to examine the relationship between the given data points. And later on, it is used in making the patterns of data, with the help of a mathematical solution. |
| There is no such assumption in Machine Learning when it comes to Data points. It is very suitable for small or large data sets irrespective of the size of the Data. The Machine Learning algorithm works very well. There is no such emphasis on this particular type of assumption. | The classic statistics always emphasize on the number of Data points, that should be more than the number of parameters to be estimated. The number of Data points should be more than the number of variables in the Data. And usually, the Data is smaller when it comes to solving it through mathematical concepts. |
| If we see the assumption of the Data generating process in Machine learning. Unfortunately, there is none because the functioning of machine learning is not to inference with the data. But to predict the events that can take place in a given data. There is no such emphasis, although it has been seen that many academics use it in theory write-ups. | Another assumption about the data generating process, that helps to know how the data has been generated. Whether it has been generated with normal probability, or by a Binomial or even by a Bernoulli. The kind of data distribution probability that is going to be suitable for a given data is something that matters a lot in statistics. The Data is gathered from careful control design. |
| When it comes to the working of Machine Learning, it works fabulously great. And it is way different than the statistics when the data is of high dimension data. Whether it is rows or columns, in terms of number of data points, number of observations, or even in the number of variables given in a Data. | The classical statistical models are designed for a few dozen variables. There can be some situations where there are a lot of variables, in this situation it performs well. But not as fluent as that in Machine learning. |
| In machine learning, we only worry about the prediction accuracy. And there is a set of the defined ways in which we will evaluate the prediction accuracy of the body. Basically, the process has to do through Cross-validation. | Basically, in the statistical model, we arrange a probabilistic model. That helps if we are doing the simple linear regression or logistic regression. Keeping all this in mind, we are assuming that this data has rendered from a normal distribution. And this has been moreover of a binomial Bernoulli kind of distribution. It also consists of the Confidence interval and P score for statistical significance. |
Final Thoughts
The technological world has been grown to the next level. That is why the battle between statistics vs machine learning is also become complex. All the sectors whereas being it an IT or industrial sector, all the records are set in the form of Data. Every company keeps a record of their Data for sales, profit, margin, and audits. And everything is set to Data. It also holds some confidential information about the company that makes it apart from the other company. We all use Statistics widely in all the companies for the mathematical solution to every problem faced by the company in their Database. Machine Learning helps in prediction making for data in each program or a language. This article discusses the most common difference between Statistics vs Machine Learning in this Artificial Intelligence world.
