Dataset
A dataset is a group of related units of facts and information that is composed of unique elements that, however, can be manipulated as a single unit by a computer. The main purpose of a dataset is to teach an algorithm of a machine how to find patterns in the whole dataset. The algorithm will then be able to predict patterns in the whole dataset.
The importance of a dataset is undeniable, so much so that to run a successful business, one has to be entirely aware of the data concerned with that business. For example, a shopping mall or even a small grocery store needs to understand the local favorites in products and also the preferences of their customers in terms of cost. Along with it, they are to know about the good and bad seasons for business to better cater to the inflow of money and attract more customers. For this, if these shops have a good and relevant dataset, they can create a good business model to run the business. Philosophically similar to this phenomenon, a good machine learning model also requires an exquisite dataset to learn and run the best algorithms for their tasks.
The common types of data include text data, image data, audio data, video data, and numeric data.
In the field of machine learning, a dataset serves two primary functions:
- To learn and improve the models in flowing consistency
- To determine the accuracy of a model after it has been educated completely
In general, the dataset is divided into three subsets as shown in Figure 3-1.
Figure 3-1Splitting a full dataset into a training set, a validation set, and a testing set
Training Set
This classification of the dataset is the most rudimentary part of it. This part of the dataset has all the information that is needed to teach and train a machine learning model. It is used to fine-tune the parameters of a machine learning model and train it by example. Training datasets are also called “learning sets.” It helps machine learning models make accurate predictions or complete tasks. Overall, this subset teaches a model how to look for the data that would make it learn better. Also, this data needs to be broken down even more before it can be used by a system. For example, a system that is good at recognizing buildings would be able to do so with the help of image data and labels that show the size and location of the building.