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Machine learning and AI are being introduced by many companies because they are improving work efficiency and creating new businesses. In machine learning, it is important to handle datasets in order to learn correctly. Some readers of this article may be interested in datasets and data formats in machine learning. So this time, I will explain about machine learning and datasets.
Data sets are essential for machine learning
Here’s an overview of machine learning and datasets.
What is machine learning?
Machine learning is one of the elemental technologies used to realize AI, and it is possible to learn a large amount of data and derive the rules and patterns hidden in that data. Machine learning can quickly perform tasks that would take an enormous amount of time for humans, leading to improved work efficiency. Besides, accurate work based on data is possible. For example, machine learning is used as an anomaly detection system at the manufacturing site. We have realized a mechanism to determine abnormalities in the equipment used in manufacturing by machine learning and notify an alert when an abnormality occurs. As a result, humans do not have to stick and monitor machines, which leads to shorter working hours.
What is a dataset?
A dataset is a set of data that is processed when learning data by machine learning. Before being used in machine learning, files handled by IBM mainframe computers are called datasets and are sometimes used synonymously with files. Data sets are an important factor in machine learning accuracy. I will explain the detailed types later.
Machine learning handles various data
Machine learning requires a variety of data depending on the problem and purpose you want to solve in your business. Since the data is repeatedly learned by machine learning algorithms, the final precision will vary depending on the quality and quantity. Machine learning mainly uses data in the following formats.
- image data
- Text data
- Video data
- Numerical data
Image data is used in image analysis. Two types of image analysis are typical, “classification” that recognizes the image itself and “detection” that partially discriminates. Text data is data composed of character strings and cannot be processed by a computer as it is. Therefore, it is processed into a form that can be understood by a computer by natural language processing. Video data is often used in voice recognition. By making the computer recognize the voice, it is used for making minutes of meetings and automatically answering telephone calls. Numerical data is the most used format in machine learning. Numerical data is being used for statistical analysis of accumulated data, detection of abnormal data, and demand forecasting.
Types of datasets in machine learning
There are three main types of datasets in machine learning:
- Test set
- Training set
- Validation set
Test set
The test set is used in the final stages of machine learning to check the accuracy of the model. In machine learning, we build models by repeating trial and error in order to improve accuracy. A test set is an indispensable data set for the final check of the accuracy of a model that has undergone repeated improvements.
Training set
Training sets are the first to be used in machine learning. It is also the largest dataset. In machine learning, first of all, it is necessary to capture a large amount of data to be learned. Therefore, the amount of data is important in the training set.
Validation set
A validation set is a dataset that is used after training a training set. Specifically, it is used for the purpose of tuning the hyperparameters of the “classifier”, which is also the parameter that controls the movement of the machine learning algorithm. The validation set may be mixed with the test set mentioned above. However, be aware that the test set is only used as a performance test.
CSV file is recommended as the data set storage format
It is recommended to save the dataset as a CSV file. There are three reasons for this.
CSV files are easy to handle
CSV is easy to handle because it is highly compatible with various systems. For example, it can be imported and output to Excel, which is used by many companies, as well as databases such as Notepad, mail software, and SQL Server. By importing it into these systems, you can view and edit the data.
Excel files are incompatible
An Excel file is a file saved with the extension “.xlsx” and is often used in spreadsheet software. Many companies use Excel, so you may want to use Excel files instead of CSV. However, Excel files are not recommended as they are less compatible than CSV files.
Let’s check the characteristics of the text file
A text file is a file that contains only textual information. Character information refers to data displayed by character codes such as hiragana, katakana, kanji, alphanumericals, and symbols. Text files, like CSV files, are a highly compatible file format that can be used by a variety of software.
Precautions when using CSV files for datasets
Keep the following points in mind when using CSV files for datasets.
Do not confuse numerical data with character data
In the CSV file, do not mix numerical data and character data in one data. Each data is described in the CSV file separated by commas, and the type is determined for each data. For example, in the case of characters, it is a type that allows a character string, and in the case of numbers, it is a type of number format. Therefore, there is a possibility that mixing will result in a string format even though you really want to process it with numbers.
Unify the meaning of the sky
In CSV files, it is important to unify the meaning of the sky contained in the data. It doesn’t matter if the data itself has empty data. However, it is important to give only one meaning to the empty dataset, such as “unmeasured”. If you include the value of 0 in the empty state, you will not be able to correctly determine the empty data.
Be careful of garbled characters
Garbled characters are states that are not displayed correctly when displayed on a computer. The CSV file may be garbled when opened by the character code. If the characters are garbled, you can solve it by opening a text editor such as Notepad, specifying a different character code, and saving.
UMWELT of TRYETING will also create the data set required for machine learning on your behalf!
Machine learning requires the dataset to be used in the correct format. However, since AI human resources with specialized knowledge are required to create datasets, there may be cases where it is difficult to prepare human resources within the company. Therefore, we recommend TRYETING’s “UMWELT”.
Since UMWELT can implement AI with no code, it can be used without tripping at the introduction stage. In addition, because it is a cloud-type tool, there is no need to build an environment and the initial cost is low, so even small companies can use advanced AI for business. In addition, with the support of specialized consultants, AI human resources will grow in-house, and in-house production will be possible in the future. In addition, it has abundant introduction results and awards, and it is a feature that it can be introduced with confidence. With Omakase Support, you can create a dataset design on Google Spreadsheet, and it will also create recipes such as the processing required for conversion of the original data.
summary
A dataset in machine learning is a collection processed by a program, and it is recommended to save it as a CSV file. Personnel with specialized knowledge are required to create datasets and handle CSV files. However, the current situation is that many companies have a shortage of AI human resources. UMWELT is an AI tool that anyone can easily use, which can perform AI analysis only by the intuitive operation of combining blocks. If you want to utilize machine learning from now on, why not consider introducing “UMWELT” introduced this time.
