Definition of a dataset in ML
Datasets are processed and structured collections of data. Every object in a dataset has its own unique properties: characteristics, connections with other objects, or a specific place in the data sample. Datasets are used in machine learning to create new hypotheses, make predictions, and train ML models.
Some common characteristics and components of a dataset include:
- Variables: different characteristics recorded in the dataset. If we take employee records as an example, variables could consist of name, age, department, and salary.
- Data Points: separate data items that are usually related in some way. In a dataset of employee records, each data point might represent a single employee.
- Format: datasets can be formatted in various ways: CSV (Comma-Separated Values), Excel files, JSON (JavaScript Object Notation), and SQL.
- Structure: data can be structured in tables, graphs, multidimensional arrays, and other forms. The structure is often determined by the type of data and its future deployment purposes.
- Metadata: provides information about the dataset, like its source, when and how it was collected, etc
The importance of datasets in machine learning
Datasets provide algorithms with the raw material that teaches them to recognize patterns and make informed decisions. If you intend on having an accurate and efficient ML model, employing high-quality datasets should be a priority. Datasets vary in nature and format – they can be numerical, categorical, time series, and in a form of text – and serve specific purposes in different machine learning applications.
Datasets are used in different steps of the machine learning process – from initial training to validation and testing of models. Sets of data help in recognising underlying patterns and trends, which are then used in predictive modeling.
The importance of datasets in machine learning cannot be overstated. They serve as a foundation to the development and success of ML models across various industries.
Main Types of Datasets
Datasets can be divided into numerous categories and subcategories based on different characteristics. Below are some of the main types of datasets in machine learning:
Based on Data Type
Numerical Dataset
These datasets are quantitative – can be defined and expressed with numbers. Numerical datasets allow for mathematical and statistical operations. Examples include temperature readings, stock prices, and student examination scores.
Categorical Dataset
These are composed of qualitative data that helps in classification tasks. Examples include datasets where elements are classified by color, gender, or occupation.
Ordered Dataset
This type of dataset has an intrinsic order, often used in ranking or priority-based analyses. Examples include customer satisfaction surveys, movie ratings, and other situations where data is ranked and prioritized.
Based on Data Source or Format
Web Dataset
Web datasets are often structured in JSON/XML formats. They are derived from web sources through APIs and used in data science projects involving real-time data analysis, market trends, social media analytics, etc. Web datasets provide a rich source of up-to-date information.
File-Based Datasets
These are typically stored in file formats like CSV or Excel files. File-based datasets are used for traditional data storage and analysis. They are easily accessible and widely compatible with data processing tools.
Based on Data Structure or Arrangement
Time Series Dataset
Introduces information as a series of data points over a certain period of time. Time series data is gathered at similar intervals and then compared. Examples include daily temperature recordings, quarterly financial earnings, or minute-by-minute stock market prices.
Partitioned Dataset
These datasets are divided into groups (partitions) based on specific criteria. This helps manage and organize large datasets. Partitioning can be based on geographical location, time periods, etc.
Based on Content Type
Text datasets
Text datasets encompass the variety of textual content – from books and scientific reports to social media posts and emails. They are used in tasks like sentiment analysis, topic modeling, and other NLP projects.
Image datasets
Image data is used in computer vision tasks: machine learning models are taught to go over visual information and interpret it correctly. Examples include databases of artwork, facial recognition datasets, and automotive testing images for autonomous driving systems.
Audio datasets
These contain sound recordings and are used in speech recognition, music analysis, analysis of sounds from surrounding environments, etc. Examples include datasets of spoken language phrases, collections of animal sounds, and sounds of musical instruments.
Video datasets
Video datasets are a combination of image and audio datasets. These datasets are essential in projects that are aimed at training models to detect movements or analyze actions. Examples include video clips for action recognition studies, movie databases for cinematic analysis, and traffic surveillance videos.
Labeled vs. Unlabeled Datasets
Datasets can also be defined by the presence or lack of labels.
Labeled data is given tags that point out the desired output for the ML model to predict. Tagged data is employed in supervised training: the ML model is trained to make predictions on input data. For instance, in image recognition, each image (data point) would be labeled with a tag describing what's in the image, like "tree" or "house".
Unlabeled data, on the other hand, is devoid of tags. It's used in unsupervised learning: the model has to find patterns and structures within the dataset without predefined rules and instructions. Unlabeled data examples include customer transaction data where the patterns of purchase need to be discovered without predefined categories. It’s easier to gather unlabeled data as it is more abundant compared to labeled data. Still, unlabeled data comes with its own challenges in deriving meaningful insights without guidance.
Characteristics of a Good Dataset
Size and quantity of data
The dataset must be large enough to capture the diversity and complexity of the problem to ensure reliable and unbiased model performance. The size and number of records should be sufficient to train the model adequately and allow for a split into training, validation, and testing sets.
Quality and cleanliness of data
High-quality data means it's accurate, complete, and relevant. The data should be free from errors, biases, and inconsistencies. Cleanliness refers to the minimal presence of noise or irrelevant information. This makes the dataset reliable and meaningful for model training.
Diversity and representativeness
The dataset should represent the real-world scenario the model aims to solve. It should consist of a wide range of examples that cover various cases and conditions that the model will encounter post-deployment. This helps in building a highly capable model that will generalize well on unseen data.
Balanced vs. unbalanced datasets
A dataset is balanced if it contains roughly equal numbers of instances in each class. Model shouldn’t be biased towards the majority in classification tasks. Unbalanced datasets require special handling and techniques to ensure fair representation and model accuracy.
Data Preprocessing
Data preprocessing is a crucial step in creating datasets and in the machine learning process as a whole. Here are the main steps:
1. Cleaning Data
This step focuses on identifying and correcting mistakes and discrepancies in the data to enhance its quality. It includes addressing missing values, eliminating duplicates, and rectifying inaccurate data entries to ensure the dataset is reliable for analysis.
2. Transforming Data
During this phase data is converted from its original raw state into a more suitable format for analysis. This could entail aggregating data, creating derived attributes, or converting data types to align with the requirements of the machine learning model.
3. Feature selection and engineering
This stage revolves around pinpointing the variables (features) that influence the model’s predictive capabilities and crafting new features from existing ones. Feature engineering involves leveraging knowledge to create features that improve the capabilities of machine learning algorithms.
4. Standardization and normalization
These methods help adjust data to a common scale, enabling accurate comparisons and ensuring equal treatment of all features by the machine learning model. Normalization typically scales the data to fit within a specified range, like 0 to 1, while standardization rescales the data to have a mean of 0 and a standard deviation of 1.
Challenges with Datasets
Common challenges with datasets in the realm of machine learning and big data include:
Data bias and fairness
This pertains to the difficulties of ensuring that the data utilized in ML and analytics accurately mirrors the real world without systematic biases. Biases within datasets may arise from various reasons: selective data collection processes, historical prejudices embedded in the data, or unrepresentative sample sizes. These biases can result in prejudiced outcomes when the data is employed to train machine learning models, impacting the accuracy and impartiality of predictions or decisions made by these models. Handling data bias and fairness is essential for constructing ethical and dependable machine learning systems that treat all individuals and groups equally.
Poor Data Quality
Data quality issues include lack of record uniqueness and referential integrity, SaaS applications are becoming increasingly popular, leading to duplicated and inconsistent records.
Handling large datasets (Big Data challenges)
With the exponential growth in data volumes, managing big data has become a critical challenge. Only 37% of enterprises employing Big Data have been successful in collecting data-driven insights. An increase in data accessibility can significantly impact a company's net income, with a 10% increase potentially leading to a $65 million increase in net income.
Data privacy and security
The vast amount of generated data has heightened concerns about data privacy and security. Moreover, the transition to cloud environments has raised concerns about data security, with 60% of corporate data now stored in the cloud.
Regulations like the General Data Protection Regulation (GDPR) have been implemented to protect individuals' data, imposing substantial fines on organizations that violate privacy and security standards.
Sources of Datasets in ML
Public datasets and repositories
Public datasets and repositories are dataset resources for individuals working in machine learning and research fields. They offer a range of data from various areas. Here are some of them:
Hugging Face
Hugging Face is closely associated with natural language processing (NLP) and deep learning, offering not only datasets, but also pretrained models and tools.
Hugging Face provides support to the machine learning community by providing datasets for tasks such as translation, auto speech recognition, and image classification. In addition to the information in the dataset card, many datasets, such as GLUE, include a Dataset Viewer to showcase the data.
Hugging Face is known for its focus on the latest advancements in AI and ML – it provides datasets that are often used in state-of-the-art research.
UCI Machine Learning Repository
UCI Machine Learning Repository has been a part of the machine learning community for years. Known for its variety of datasets used in academic and research environments, it serves as a great data source for each and every purpose. UCI Machine Learning Repository includes datasets ranging from simple toy datasets to complex real-world data, supporting both educational and advanced research needs.
This repository categorizes datasets based on the type of ML task they fit best (e.g., classification, regression), making it simpler for researchers to locate data for their studies.
Kaggle
Kaggle is a big and supportive AI and ML community. More than 5 million data scientists share and stress test all the latest ML techniques. This platform holds more than 299,000 datasets and 2,300 pre-trained ML models. Kaggle’s datasets can be used across various fields – from healthcare to retail. It’s a useful platform where industry professionals can analyze and exchange datasets and insights, creating a community aimed at learning and uplifting each other.
Synthetic datasets
Synthetic data is artificially generated information. It has no connection with real-world events. Synthetic data is created algorithmically to mimic real datasets. Generating synthetic datasets involves either simulating data based on real-life situations or creating it from scratch to fulfill the project's requirements. Techniques, for generating datasets consist of:
- Utilizing statistical models to produce data that adheres to a particular distribution or pattern. This approach is beneficial in cases where concerns about privacy or limited data availability hinder the use of real data.
- Employing generative models, such as Generative Adversarial Networks (GANs) which have the capability to create images, texts, or any other type of data by learning from authentic datasets.
- Using simulation methods that replicate systems or environments to produce data that would be impractical or unattainable to gather in the real world.
Check out our article on synthetic data generation to learn more.
Proprietary and in-house datasets
Proprietary and in-house datasets are used by companies internally. These datasets contain information that sheds light on business operations, customer behavior patterns, and market trends. Here are some key points about these datasets:
- They are often the result of extensive data collection efforts, including customer interactions, transaction histories, and operational data collected meticulously during business activities.
- Such datasets can give companies a competitive advantage – they are unique to the organization and can be customized to meet business requirements using machine learning solutions.
- Managing these datasets involves dealing with challenges related to data governance, privacy laws, and ensuring the quality and reliability of the data.
Conclusion
Datasets are an integral part of the whole machine learning process. They are used in every step of the way: during training, validation, and testing stages. Moreover, datasets are then used for continuous, iterative learning of models to consistently improve their capabilities.
