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Machine learning is closely related to fields such as “AI,” “artificial intelligence,” and “deep learning,” which have received a great deal of attention in recent years. All of these latest technologies aim to realize more efficient work and society by using systems, but the mechanism includes many complicated ideas. In this article, I will explain “regression”, which is an indispensable idea in machine learning.
What is regression in machine learning?
The word regression may not be very familiar to anyone who has never learned statistics. Regression is necessary knowledge to utilize machine learning, so let’s learn machine learning step by step by first understanding the meaning of this word. In the following, we will explain the outline of regression.
What is machine learning?
Machine learning is one of the methods to analyze a large amount of data such as audio and video. Iterative learning is performed from the data, and the learning result is “ruled = modeled” to capture the characteristics and trends of a certain event. Next, you will be able to “automate” the ruled features and trends, and then follow those rules from the next time onwards.
What is regression?
Regression is one of the common types of machine learning models. It refers to predicting the next value for consecutive input values, and can be used to estimate the cause for the result, for example, when analyzing the relationship between advertising expenses and the number of visitors by converting it into a number. When there is one explanatory variable that causes it, it is called simple regression analysis, and when there are multiple explanatory variables, it is called multiple regression analysis.
Difference from classification
What is often confused with regression is “classification”. Classification is a model that can predict the next value like regression, but the nature of the data that can be analyzed is different. For example, regression analysis can analyze a customer’s past buying behavior to predict how many times the customer will buy a new product. On the other hand, in the case of classification, it is possible to predict whether or not the customer likes the new product from the purchasing behavior data.
Types of regression analysis
Regression analysis is used to predict future demand. With it, you can predict not only future demand, but also sales and business prospects. From here, I will explain two types of such regression analysis.
Simple regression analysis
Regression analysis is a method of investigating the relationship between the resulting numerical value and the factor numerical value and clarifying the relationship between them. The numerical value that causes the factor is called the “explanatory variable”, and the numerical value that results is expressed as the “explained variable”, but the analytical model with one “explanatory variable” is called the “single regression analysis”. For example, a simple regression analysis model can determine weight from height data.
Multiple regression analysis
Those with multiple “explanatory variables” mentioned above are called “multiple regression analysis”. In the case of height, multiple regression analysis requires data such as height, abdominal circumference, and chest circumference to obtain weight data. You can think of this method as an extension of simple regression analysis. Multiple regression analysis is an ideal model that can capture multiple variables and handle complex elements, but it also increases the cost of building it.
What Regression Analysis Gives to Business
Data analysis is a technology that can create new value from a wealth of information, but what can be done by applying this to your business? Below, we’ll discuss the changes that regression analysis can bring to your business.
marketing
Logistic regression analysis is often used in marketing. Logistic regression is a model that solves a classification problem and outputs which class the input is classified into and how likely it is to be classified when the input is given. An example of actual use is a system that predicts potential diseases by examining the correlation between disease incidence rates from quantitative values of lifestyle-related habits.
Data scientist
Professionals who utilize data analysis to support decision-making from a scientific point of view are called “data scientists”. Many companies have not yet secured such human resources, and many companies lack human resources who specialize in data science. Securing or training human resources with such expertise should have a great effect on the company’s business in the future.
Precautions for regression analysis
In order to utilize regression analysis efficiently, let’s understand the points that you want to suppress in advance. Here are some tips for avoiding problems that tend to occur when actually building a data analysis platform.
Professional knowledge is required
This is not limited to regression analysis, but data analysis requires specialized knowledge. High expertise is indispensable for building and operating a data analysis platform. Therefore, it tends to be personalized in the form of being used only by some data engineers with specialized skills. If you remain personalized, it is likely that you will not be able to take over due to the retirement or transfer of the person in charge, and it will be difficult to continue data analysis.
Human error is likely to occur
In machine analysis, it is possible to make predictions on a scale that cannot be done manually by processing a huge amount of data, but it is necessary for humans to judge the data to be actually used. If the verification stage contains data that is difficult to analyze, the accuracy of the system may decrease, and if extra data is included, it may cause a decrease in accuracy. Therefore, processing such as excluding unused data each time is indispensable, but since this process must be performed manually, human error is likely to occur.
Depends on the quality of the data
Since big data is a diverse group of data, it is inevitable that it contains data that does not fit the purpose in the first place, and that there are typographical errors, duplications, and omissions. Therefore, it is necessary to standardize = cleanse the data according to the purpose before analysis. Failure to do this pre-processing can lead to erroneous analysis results.
Equipped with abundant functions
UMWELT is equipped with a large number of machine learning algorithms, and by freely combining them, it is possible to build “any data”, “easy”, and “advanced” algorithms. Just like a Lego block, you can create a data analysis construction with AI just by combining functions, so no difficult knowledge is required.
Save money and time
UMWELT acquires data by RPA (Robotic Process Automation, a technology that automates routine tasks with robots), performs automatic machine learning, and outputs the data. This enables highly accurate analysis from past sales data for decades and tens of thousands of product numbers that cannot be considered by humans. By combining the set-up algorithms, it can be used at the industry’s lowest level of compact cost, and since the existing system is not modified, the cost of troublesome in-house adjustment is also minimized.
AI human resources can also be trained
With UMWELT, it is possible to develop AI human resources. After the introduction, a TRYETING consultant will provide accompanying support, so even those who do not have knowledge of data analysis infrastructure or programming can use it with confidence. An optional training menu is also provided so that the staff in charge can gain knowledge and experience of AI.
summary
Nowadays, the promotion of DX is being called for, and many companies are keenly aware of the need for data analysis such as regression analysis. On the other hand, there are many people who say, “I want to introduce useful AI to my company, but I don’t know what to start with” or “I’m in trouble because I don’t have specialized knowledge.
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