What are Machine Learning Datasets? A Beginner’s Guide

In the world of AI and Machine Learning,  you will hear one term over and over again: Dataset. But what we need to know is, what exactly are Machine Learning Datasets, and what are their types? If you are new to this, it is better to have a basic understanding of it.

Firstly, we need to know what a dataset is. In simple words, a Dataset is a collection of data that is used to train, test or evaluate algorithms and models. An algorithm is a set of rules or instructions that a computer or program follows to solve a problem. And the model is the output of that algorithm after it has learned from the dataset. That it.

Now, let’s understand how it stands with Machine learning.

What is a Machine Learning Dataset?

A Machine Learning Dataset can be described as a huge spreadsheet or an organised folder of files. In essence, it is a structured collection of data. This data can be anything from images and text to numbers or audio clips. This is what machine learning models use to learn the patterns and make decisions.

As we said here, structured data, which is the precise form a ML dataset is supposed to be. Any raw or unstructured data isn’t a data set. These should be organised and labelled with complete information that a model could understand.

Types of Datasets in Machine Learning

In machine learning, a dataset is a collection of data that an algorithm uses to learn from, validate and test the performance. The nature of the data directly influences the machine learning task and model. The datasets are classified based on structure and purpose. We must understand what these classifications are before preparing data and building effective models.

Structure-Based Machine Learning Datasets Types 

We already discussed how a dataset is supposed to be structured for the Machine Learning Models. There are different types depending on the type of data it contactins and is used for.

• Structure Datasets: The datasets that are organised in rows and columns like a spreadsheet or database table. It is one of the most common types of datasets. It follows a fixed rule, which is stored in a table with rows and columns. Each of the fields is defined with a data type, which makes it adaptable for query and analysis.
• Unstructured Datasets: Unlike a structured dataset, it does not have a pre-defined format. It is harder to work with, but with the right tool, we can work with this dataset. The ultimate tools are recommended in the coming section.
• Semi-Structured Datasets: It is a mix of the two; it has some organisational properties, but not as rigid as a structured dataset. Examples would be JSON, XML, and CSV.

Purpose-Based ML Dataset Types

Now we know that a Machine learning dataset isn’t something simple or one type of dataset; they are typically split into 3 parts or types based on the role of the dataset in machine learning.

• Training Dataset: As the name suggests, it is where ML models learn from. Being one of the largest parts of the dataset, comprising 70-80% of the total data. The data is fed into the algorithm, allowing it to learn the relationship and patterns to build its predictive capability. That is, the model is essentially being shown thousands of examples until it finds the pattern on its own.
• Validation: It is often the smaller subset of the original data. The validation dataset is used to fine-tune the model’s parameters and prevent overfitting. Overfitting occurs when a model becomes overly specialised in a specific task, rendering it ineffective when applied to new data sets. With a validated Machine learning dataset, we can effectively test different versions of the model as you develop it. This is to ensure you select the best-performing one before proceeding to the final evaluation step.
• Testing: After the model is trained and validated, the test set is used to evaluate the model’s performance. We keep the data set aside during the training process to check how well the ML model works. That is the accuracy, precision, recall, F1-score, etc. It is 20% of the total dataset(the exact split depends on dataset size and the project). In simple terms, you train a model with one set of data and then test it with a separate set to see the accuracy of the ML model.

Why Use a High-Quality Dataset in Machine Learning?

An ML model is only as good as the data it’s trained on. The quality of the dataset directly impacts the quality of the model. It learns by identifying the relationships and patterns in the data it’s provided. The following could determine what a good Machine learning dataset is:

• Accurate: The data and the label should be correct. Incorrectly labelled data would confuse the model, which would thereby cause it to learn wrong patterns.
• Diverse and Representative: It should reflect the real-world scenarios so that the model will perform well on new, unseen data. For instance, if the model is for facial recognition and is trained on images of adults, it may not perform well when identifying children.
• High-Quality: The data should be accurate and free of errors. Missing any values, duplicates, or inconsistent data can lead to poor performance by the model.
• Balances: The data should be unbiased. It should represent all the scenarios. An unbalanced or biased dataset would make the model work better for one group and poorly for another.
• Sufficiently Large Enough: Generally, the more the better. The model needs enough data to learn the pattern efficiently. A large set of data allows the model to train better in the training phase and gives more examples for the model to learn. It reduces the risk of overfitting and generalises the new, unseen data.

Tip: For those working on large datasets, a tool like CSV Splitter is essential for managing and preparing the data.

A high-quality machine learning dataset is crucial. It is the foundational element that determines the outcome of the machine learning project.

FAQ

Q) What is the difference between a dataset and a Machine learning dataset?

A) A dataset is a collection of data. A Machine learning dataset is a specific type of dataset that is collected, organised, cleaned and labelled for Training, validating and testing an AI model.

Q) How can I create datasets for Machine learning?
A) First, gather the raw data from any sources, like emails, databases, webpages, etc. The cleaning and preprocessing are used to handle errors and missing values. Then convert the cleaned data to a structured format like CSV or JSON, making it work for a model. For the ML Model learning task, label the data and then split it into training, validation and testing sets.

Q) Are machine learning datasets always in CSV format?

A) No. Machine learning datasets come in different which also include CSV(Comma-separated values). CSV is a popular format due to its simplicity and universal compatibility. There are other formats such as JSON, Images(JPG, PNG), Audio files, Parquet and HDF5 for large-scale data and scientific computing.

Conclusion

A machine learning dataset is one of the essential parts of the world of artificial intelligence. We have explored from a simple dataset to an ML dataset, as it is far more than a simple collection of data; it is what makes the AI model intelligent. Without a high-quality dataset, the most advanced algorithm is the dataset. The quality, accuracy and structure of the data directly determine the outcome of the machine learning project. As it is essential to advance in AI, it is equally important to properly create datasets.