Regression models, which are supervised learning methods, are used in the field of many companies such as marketing research. In this article, we will introduce specific examples and usage scenes for companies who want to learn what can be achieved with regression models.
What is a machine learning regression model?
As you learn about machine learning, you will have the opportunity to come into contact with a variety of jargon. In particular, regression model is a term that has an important meaning as one of the classifications of machine learning. First, after reconfirming the definitions of machine learning and models, we will explain terms such as regression models and classification models.
What is machine learning?
Machine learning is a data analysis method that finds some regularity or rules by iteratively learning past cases and data. It is also considered to be one of the methods for realizing AI, which is a computer that imitates a part of human intellectual behavior. In machine learning, the regularity gained from learning is applied to unknown and future cases to make predictions and inferences. In the business scene, it is used in roles such as automation of routine work, demand forecasting, and recognition / identification of voice and image data.
Machine learning is classified into three types, “supervised learning,” “unsupervised learning,” and “reinforcement learning,” depending on the method. Supervised learning is a method of learning the correct data from a computer and producing the correct output for the input data. On the other hand, unsupervised learning does not set the correct data, so the computer itself learns the characteristics of the data through a large amount of data. Reinforcement learning is a learning method in which the computer determines that the numerical value of the scored output result is the highest. In addition, deep reinforcement learning that combines deep learning and reinforcement learning, and deep reinforcement learning, which is a learning method when a small number of labeled data and a large amount of unlabeled data are prepared, are often cited as machine learning methods. Will be.
What is a model in the first place?
A model in machine learning is a mechanism that outputs the result for input data. The input data is analyzed and evaluated / judged based on some evaluation criteria. The sequence of machine learning processes is “input-> model-> output”. There are several types of models, which can be used according to the characteristics of the data and the purpose of machine learning. Of these, the regression model and the classification model are the two most frequently used.
What is a regression model?
Regression models are supervised learning techniques used to predict some value. Regression models enter contiguous values to make predictions about the future and unknown cases.
What is a classification model?
The classification model is a method used for the purpose of selecting the most suitable one from several options and for the purpose of categorizing items. In the classification model, classification is performed by inputting non-contiguous values called discrete values.
What is analysis using a regression model of machine learning?
In the regression model, the value y you want to output is called the objective variable, and the value x for predicting this is called the explanatory variable. Depending on the number of this explanatory variable, it can be classified as either simple regression analysis or multiple regression analysis. Now let’s take a closer look at the specific characteristics of these analytical methods.
Simple regression analysis
Simple regression analysis is a regression model in which the explanatory variable y is fixed to one value. Expressed as an expression, y = ax + b, and the graph is linear. It is also called linear simple regression analysis because of its linear graph characteristics. Simple regression analysis alone cannot identify the causal relationship between cause and effect, but it is a clue to reasoning.
An example of utilization in the business scene is the case of inferring the objective variable of “high rent” using the explanatory variable of “size of residence”. The advantage of simple regression analysis is that the values scattered in the scatter plot can be converted into a linear graph for visualization.
Multiple regression analysis
In multiple regression analysis, there are multiple explanatory variables instead of one. Therefore, unlike simple regression analysis, linear graphs and y = ax + b cannot explain the objective variable, but more advanced inference and analysis are possible.
As an example of utilization in the business scene, there is a case where the objective variable “high rent” is inferred from multiple explanatory variables such as “size of residence”, “age”, and “distance from the station”. The advantage of multiple regression analysis is that you can screen out explanatory variables that are likely to have a significant impact on the objective variable.
Regression models are widely used in all business situations as a method that plays a particularly predictive role in machine learning. Here, we will take a closer look at what kind of content can be concretely realized by the regression model of machine learning.
marketing
Marketing is a mechanism to find out “what value a company can provide to meet the needs of the market and individual customers”. Generally, marketing is done by marketing research, advertising, and data analysis.
When utilizing machine learning regression models in the marketing field, computers automatically learn customer information, past purchase data, and market fluctuation data to identify factors that can lead to sales growth. Then, it predicts future demand fluctuations and changes in customer purchasing behavior when a company takes some action.
Many companies have already embraced marketing research and data analysis, but replacing this task with machine learning will allow us to analyze more data faster. In addition, it enables more accurate analysis than humans do, leading to improved marketing effectiveness and operational efficiency.
Medical forecast
Machine learning regression models are being used not only in the business scene but also in the medical field. For example, it has been introduced for the purpose of preventing the onset of illness in patients and simulating the effects of medication. Specifically, it is an example of utilization such as analyzing an image of a patient’s test result to predict a disease that is likely to be a disease, or predicting the effect of a specific treatment or medication on a patient. In addition, it is possible to predict the next year’s medical expenses based on the patient’s health diagnosis results and past medical expenses information.
Utilizing machine learning regression models for medical prediction has various benefits such as reducing the number of patients with serious illnesses, promoting the proper use of medicines, and improving the work efficiency of medical staff.
Device abnormality detection
Various facilities and equipment are in operation at various factories and production lines. Since these devices sometimes behave abnormally for some reason, a system to detect them is installed on the production line.
In the regression model of machine learning, the state of a normal device is defined, and then the difference from the waveform produced by the device in an abnormal state is learned. As a result, it becomes possible to automatically detect abnormalities in equipment, leading to improvements in productivity and operational efficiency.
Utilization points of regression model of machine learning
By utilizing the regression model of machine learning, you can expect benefits such as operational efficiency and sales expansion, but there are points to be aware of in order to achieve results. What specific points do we need to pay attention to when utilizing the regression model of machine learning?
Beware of overfitting
Overfitting is a phenomenon in which overfitting to the training data in a machine learning regression model deviates from the tendency that the data originally suggests. For example, the training data used in the regression model has outliers, noise, and bias, but passing through all of these values results in a complexly bent graph.
Overfitting makes it impossible to predict the future or unknown cases because it is impossible to understand what the meaning and trends of the entire data are. Ways to avoid overfitting include increasing the training data as much as possible and penalizing the model as it becomes more complex.
Watch out for black box issues
The black box problem is a phenomenon in which the reason and background of the result of machine learning performed by AI cannot be understood. For example, if a black box problem occurs when predicting or making a judgment on a disease in the medical field, it becomes impossible to explain to the patient why the name of the disease was derived.
As a workaround for the black box problem, for example, when performing image recognition, there is a method of repeating the work of recognizing only a part of the image and comparing the difference with the result of recognizing the entire image. Through this work, it becomes possible to understand which part of the data used for judgment was the focus of AI.
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summary
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