Before we jump into the specifics of what type of datasets can be used for machine learning and data science models, let us examine what types of datasets do machine learning and data science models require. It is a general belief that machine learning and data science models can take whatever type of data is being fed into them. This is false. Machine Learning and Data Science models require a very high quality of data to accurately represent or interpret any type of data that they have in them.
Datasets are needed in today’s modern technological world for various reasons. Now we must wonder what type, categories of datasets can these advanced models read accurately?
What Should Datasets Consist Of?
There are three different characteristics of datasets, namely- Dimensionality, Sparsity, and Resolution.
Dimensionality: It refers to how many attributes a dataset has. Also, it refers to techniques that reduce the number of input variables in a dataset.
Sparsity: Data sparsity is the term used for how much data we have for a particular dimension/entity of the model.
Other than this, datasets should contain the data extracted from web scrapers that scrape different websites for information needed by clients and customers.
The datasets have several characteristics that define a dataset’s structure and properties,including different numbers and types of variables and statistical measures that apply to them.
How Large do the Files have to be for Machine Learning and Data Science?
There is no size requirement per se for any dataset fed into the algorithm. But there are however minimum requirements that are a basic necessity for the algorithm to perform. It also depends on the complexity of the problem that you are trying to solve. Buy datasets that are structured data sets from websites spanning across domains like Retail, Healthcare, Recruitment, Travel, Classifieds, and more. These data sets result from high-quality web scraping, refining, and structuring, which means the data you get is of top-notch quality.
Why is this Question being raised?
Too much data at hand? Then it would help if you considered developing different learning curves that will help you find out how big the representative samples are. You can always use the big data framework to use all the available data.
Too little data? Then you will have to confirm that you have fewer data in your possession and consider collecting more data from different trusted resources to increase the sample size with you.
With this being said, we come back to the original question as to “Why do you need so much data
1). No one can tell you this
You may or may not discover the answer to this question through different empirical investigations. The sheer complexity of the problem itself is unknown to the variable’s input and output’s underlying function.
2). Analogical Reasoning
There may have been many people who have worked on machine learning and data mining before you. Check out their published results to know more about them. You can perhaps look at their studies and perform your own analysis using the algorithms and scales with different dataset sizes.
3). Algorithms that are Nonlinear Need More Data
The more powerful Machine Learning gets, it may sometimes become rendered as a nonlinear algorithm. These are some flexible algorithms that are even nonparametric values. If all the linear algorithms achieve good performances overall, there will be hundreds of examples of this type of algorithms at your disposal.
4). Evaluate Dataset Size vs Model Skill
While developing any new machine learning and data science algorithm, you will have to demonstrate and explain the said algorithm’s performance concerning the amount of data or the complexity of the problem. The only suggestion we can make here is to perform your own study with all the available data and use the single most performing algorithm to showcase and get the accurate results in your study.
Datasets may have multiple fields, and some of them won’t even have the data you are looking for, this is where Machine Learning and Data Science comes in and helps users get accurate results from the extracted data.
If you liked reading the content above as much as we enjoyed writing it. We are sure you will like to read How data has become the New Oil of Modern Technology.