The MORPH-II Dataset: A Verified Resource for Facial Recognition and Demographic Analysis
The MORPH-II dataset is a widely used and highly regarded dataset in the field of facial recognition and demographic analysis. Developed by Dr. Karl Ricanek and his team at the University of North Carolina Wilmington, the dataset was first released in 2006 and has since become a benchmark for evaluating the performance of facial recognition algorithms. In this article, we will discuss the MORPH-II dataset, its features, and its applications, as well as provide verification details to ensure its accuracy and reliability.
What is the MORPH-II Dataset?
The MORPH-II dataset is a large-scale collection of facial images, consisting of over 55,000 images of 13,000 individuals. The dataset is diverse, with images of people from various ethnicities, ages, and genders. The images are 24-bit color, 256-tone grayscale, and range in size from 128x128 to 240x320 pixels.
The MORPH-II dataset was created to support research in facial recognition, demographic analysis, and other related fields. The dataset is particularly useful for studying the effects of aging on facial appearance, as well as for developing algorithms that can accurately recognize and classify faces across different demographics.
Features of the MORPH-II Dataset
The MORPH-II dataset has several key features that make it a valuable resource for researchers:
Applications of the MORPH-II Dataset
The MORPH-II dataset has numerous applications in:
Verification Details
To ensure the accuracy and reliability of the MORPH-II dataset, several verification steps have been taken:
Verified Statistics
Several studies have been conducted to verify the statistics of the MORPH-II dataset. For example:
Conclusion
The MORPH-II dataset is a verified and widely used resource for facial recognition and demographic analysis. Its diversity, large scale, and variability make it an excellent resource for researchers and developers. The verification details and statistics provided in this article demonstrate the accuracy and reliability of the dataset. As a result, the MORPH-II dataset continues to be a benchmark for evaluating the performance of facial recognition algorithms and a valuable resource for research in computer vision, biometrics, and demographic analysis.
References
Availability
The MORPH-II dataset is publicly available for research purposes. Interested researchers can access the dataset by contacting Dr. Karl Ricanek or through the MORPH-II dataset website.
The MORPH-II dataset is one of the most widely recognized longitudinal face databases used for research in facial age estimation, gender classification, and race recognition. Created by Ricanek and Tesafaye, it was developed to address the limitations of smaller datasets by providing a massive corpus of images documenting adult age progression. Overview of MORPH-II
Released in 2008, the non-commercial version of MORPH-II contains approximately 55,134 unique facial images (primarily mugshots) of 13,000 subjects. Key characteristics include:
Longitudinal Span: Images were captured between 2003 and 2007, with some individuals appearing multiple times, allowing researchers to track aging over several years.
Demographic Variety: The subjects range in age from 16 to 77 years and include diverse ethnic backgrounds such as African, European, Asian, and Hispanic.
Rich Metadata: Each image is accompanied by metadata for age, gender, and race, facilitating high-accuracy classification studies. The "Verified" Aspect: Cleaning and Validation
While MORPH-II is a benchmark, researchers have identified that much of its raw metadata was originally self-reported, leading to inconsistencies in recorded ages or demographic data. To ensure the data is reliable for scientific use, "verified" versions or cleaning protocols have been established:
Data Cleaning Whitepapers: Research teams at UNC Wilmington and other institutions have published "cleaning" strategies to correct these inconsistencies.
Verification Scripts: Publicly available repositories, such as the MORPH Subgroups and Cleaning script on GitHub, provide tools to filter and verify age ranges, gender, and ethnicity before training models.
Standardized Protocols: Projects like morph2-protocols offer verified "splits" (e.g., the Random, Whole, and AGR protocols) to ensure researchers can replicate and benchmark their studies using the exact same, validated data subsets. Applications in Modern Research arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
MORPH II dataset (Multi-Objective Risk Estimator) is one of the most significant longitudinal face databases in computer vision, widely recognized for its high-quality mugshot images used in facial recognition, age estimation, and demographic classification. Released primarily through the University of North Carolina Wilmington (UNCW)
, it contains over 55,000 images of more than 13,000 unique subjects, captured between 2003 and 2007. Core Attributes and Composition
The dataset is characterized by its "longitudinal" nature, meaning it tracks the same individuals over time (spans ranging from months to several years), which is critical for studying the biological aging process. Demographics:
The database includes diverse ancestry, primarily African (77%), European (19%), and smaller percentages of Asian, Hispanic, and Indian descent. Each entry is accompanied by rich metadata, including Subject ID Date of Birth Date of Arrest (varying from 16 to 77 years). Technical Specs:
Images are typically provided as 8-bit color JPEGs, often cropped and aligned for immediate use in machine learning pipelines. The "Verified" Aspect: Cleaning and Inconsistencies
The term "verified" in the context of MORPH II often refers to research efforts to address and correct data inconsistencies found in the original releases.
[1811.06446] Preliminary Studies on a Large Face Database - arXiv
Understanding the MORPH II Dataset: Why "Verified" Matters In the world of facial recognition and biometric research, the MORPH II dataset stands as one of the most critical benchmarks for longitudinal studies. Whether you are developing algorithms for age progression, facial recognition, or demographic estimation, the integrity of your data determines the accuracy of your results.
However, researchers often search for "MORPH II dataset verified" versions to ensure they are working with the highest quality data. Here is a deep dive into what makes this dataset unique and why verification is a non-negotiable step for modern AI development. What is the MORPH II Dataset?
Created by the Face Aging Group at the University of North Carolina Wilmington, the MORPH (Metamorphosis) database is one of the largest publicly available longitudinal face databases. The Academic Edition (MORPH II) contains: Images: Approximately 55,000 images. Subjects: Roughly 13,000 unique individuals.
Span: Images captured over several years, allowing for aging analysis.
Metadata: Includes age, sex, and ethnicity (Black, White, Asian, Hispanic, and "Other"). Why Use a "Verified" Version?
In large-scale datasets, "noise" is inevitable. Raw data often contains inconsistencies that can skew machine learning models. A verified MORPH II dataset typically refers to a version where the following issues have been addressed: 1. Identity Consistency
In unverified sets, a single individual might be assigned two different ID numbers, or two different people might be grouped under one ID. Verification involves manual or algorithmic cross-referencing to ensure that every "subject" is truly unique and consistent throughout their aging sequence. 2. Accurate Metadata morph ii dataset verified
Age and ethnicity labels in the original metadata can sometimes contain clerical errors. A verified dataset cross-checks the capture dates against the birth dates to ensure the "Age" label is mathematically correct for every frame. 3. Image Quality Control
Verification often includes filtering out images with extreme poses, heavy occlusions (like hands over faces), or poor lighting that could break a facial landmark detection algorithm. The Role of MORPH II in Modern AI
The "verified" MORPH II dataset is the gold standard for three specific areas of research:
Age Invariant Face Recognition (AIFR): Training models to recognize a person even if their last photo was taken ten years ago.
Age Estimation: Teaching AI to guess a person’s age within a narrow Mean Absolute Error (MAE).
Demographic Bias Mitigation: Because MORPH II has a significant representation of different ethnicities (particularly Black and White subjects), it is frequently used to test if an algorithm performs equitably across different races. How to Access Verified Data
It is important to note that the MORPH II dataset is not open-source in the traditional sense. It requires a formal Data Transfer Agreement (DTA).
Request Access: Researchers must apply through the UNCW Face Aging Group.
Verify the License: Ensure your institution has signed the necessary paperwork to use the data for non-commercial research.
Preprocessing: Many researchers use third-party scripts (available on platforms like GitHub) to "verify" and clean the raw files once they have legally obtained the images. Conclusion
Using a verified MORPH II dataset is the difference between a model that works in a lab and a model that works in the real world. By ensuring identity consistency and metadata accuracy, researchers can push the boundaries of biometric technology without the interference of data noise.
The MORPH II Dataset is one of the most significant and widely cited longitudinal face databases in the world, primarily used for research in age progression, facial recognition, and demographic estimation. To be "verified" typically refers to the rigorous process of gaining authorized access to this sensitive biometric data through the Face Aging Group at the University of North Carolina Wilmington (UNCW). 1. Longitudinal Depth
The hallmark of MORPH II is its longitudinal nature. It contains over 55,000 images of approximately 13,000 individuals taken over multiple years.
Time Spans: The interval between the earliest and latest photos of a single subject can span up to several decades.
Verification Utility: This allows researchers to verify the performance of facial recognition algorithms as a person ages, a phenomenon known as "age-invariant face recognition." 2. Demographic Diversity
Unlike many earlier datasets that lacked diversity, MORPH II provides a broad demographic spread, making it essential for testing algorithmic bias.
Ancestry: It includes significant representations of Black, White, Hispanic, Asian, and "Other" ethnicities.
Gender: It contains images of both male and female subjects.
Metadata: Verified users get access to precise metadata, including chronological age, gender, and ancestry labels for every image. 3. Real-World "Non-Cooperative" Conditions
While the images are captured in a controlled mugshot format, they reflect real-world conditions better than laboratory-only sets.
Variations: The dataset includes natural variations in lighting, facial hair, weight gain/loss, and minor pose shifts.
Verified Quality: Every image in the collection is manually reviewed to ensure it meets the database's standards for research-grade biometric analysis. 4. Controlled Access & Ethical Compliance
Access to the MORPH II dataset is not public; it requires a formal verification process.
Legal Agreement: Researchers must sign a Data Use Agreement (DUA) ensuring the data is used for non-commercial, academic research only.
Institutional Oversight: Verification usually requires a sign-off from a university's Institutional Review Board (IRB) or a department head to ensure ethical handling of the subjects' identities. 5. Benchmark Performance
Because it is a "verified" standard in the industry, MORPH II serves as a primary benchmark for state-of-the-art AI models.
Age Estimation: It is the gold standard for training models to predict a person's age from a photograph.
Commercial Validation: Many commercial facial recognition systems use MORPH II to verify that their software remains accurate even as users grow older.
MORPH II dataset (released in 2008) is a landmark longitudinal face database widely used for facial recognition, age estimation, and gender/race classification. While it remains a benchmark in computer vision, its "verified" status refers to both the commercial/academic verification of users and the ongoing research to clean and verify the internal data itself. Dataset Overview Composition : The 2008 non-commercial release contains 55,134 mugshots from approximately 13,000 subjects. Longitudinal Depth
: Images were captured between 2003 and late 2007, often featuring the same individuals arrested multiple times over several years. Demographics
: Includes subjects aged 16 to 77 of African, European, Asian, and Hispanic descent. Key Metadata
: Each entry typically includes age, gender, race, height, and weight. The "Verified" Status
The term "verified" in the context of MORPH II often pertains to two specific areas: Access Verification : MORPH II is not an open-source download. Researchers must apply for access through official channels, typically managed by the University of North Carolina Wilmington (UNCW) , which provides both Academic and Commercial editions. Data Inconsistency & Cleaning
: Although the data is sourced from real mugshots, a notable whitepaper, "MORPH-II: Inconsistencies and Cleaning,"
revealed that because much of the original data was self-reported by arrestees, researchers have had to manually verify and "clean" errors in age and demographic labels to ensure accurate algorithmic training. Modern Applications in Morphing Research
Researchers frequently use MORPH II as a foundation to create "verified morphing attack"
datasets. Because the original MORPH II subjects have multiple longitudinal photos, they provide a "bona fide" (authentic) baseline for testing how well biometric systems can distinguish real aging from a "morphed" photo. MorphAge Dataset
: A specialized subset derived from MORPH II specifically to study the influence of aging on face morphing detection.
: A more recent synthetic dataset (2024) that uses identities and patterns from benchmarks like MORPH II to generate over 100,000 high-quality morphs for training attack detection systems. Access and Protocols
For standardized results, the research community uses specific protocols: AGR Protocol The MORPH-II Dataset: A Verified Resource for Facial
: Balances male-to-female and white-to-black ratios for unbiased age estimation. RANDOM Protocol
: A simple 80/20 training/testing split, though it is often criticized for lack of reproducibility. official application process to obtain the MORPH II dataset for a research project? AI responses may include mistakes. Learn more arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
dataset is a massive longitudinal collection of adult face images frequently used for biometric research, specifically in age estimation, gender and race classification, and morphing attack detection. ResearchGate Key Highlights of MORPH-II Massive Scale : It contains approximately 55,134 unique images of 13,000 subjects. Demographic Diversity : The subjects include individuals from African, European, Asian, and Hispanic ethnicities, with ages ranging from 16 to 77 years Longitudinal Aspect
: Because it includes many images of the same individuals arrested multiple times over a five-year span (2003–2007), it is a gold standard for studying how faces age over time in digital systems. "Verified" & Cleaned Versions
While the original dataset is popular, researchers have identified "interesting" inconsistencies—such as self-reported age and gender errors. This has led to the creation of verified subsets University of North Carolina Wilmington | UNCW MORPH-II Inconsistencies and Cleaning : A notable whitepaper from details the process of correcting these errors. MORPH Subgroups and Cleaning : Available on
, this repository provides scripts to clean age metadata specifically to test if face recognition accuracy improves or degrades with age. Train/Val/Test Splits
: Pre-verified splits (typically 80-10-10) are often hosted on platforms like
with labels already provided in CSV format for immediate use in machine learning. Recent "Interesting" Applications Morphing Attack Detection (MAD)
: Researchers use MORPH-II to create "morph" images (merging two people's faces) to see if they can fool biometric systems into verifying both identities. Age Estimation Benchmarking
: It is a primary benchmark for testing AI's ability to predict a person's age within a 5-year margin of error Synthetic Augmentation : New datasets like
use MORPH-II as a "non-synthetic" baseline to compare against high-quality GAN-generated faces. used to clean this data or how to gain access to the official non-commercial version? arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
The MORPH II (Verified) dataset is a landmark longitudinal face database used primarily for research in age estimation, face recognition, and biometric forensics. While the original MORPH ( Craniofacial Longitudinal Morphological Face Database) was released in 2006, the "Verified" subset of MORPH II refers to a cleaned, high-integrity version where metadata and identities have been rigorously cross-checked for accuracy. 1. Dataset Overview
The MORPH II dataset is the largest publicly available longitudinal face database. It is designed to help researchers understand how facial features change over time due to aging and how those changes affect automated recognition systems.
Size: Contains approximately 55,134 images of about 13,000 individuals.
Time Span: Longitudinal coverage ranges from a few months to over 20 years between the first and last captures of a single subject.
Demographics: Includes a diverse mix of ethnicities (predominantly Black and White) and genders, though it is often noted for having a higher representation of male subjects. 2. What "Verified" Means
In the context of MORPH II, "Verified" denotes a specific subset or a refined state of the data used in formal academic benchmarks.
Identity Integrity: Every image is linked to a unique subject ID that has been manually or algorithmically verified to ensure no "identity leakage" (where different IDs are actually the same person) occurs.
Metadata Accuracy: Each image is tagged with "ground truth" data, including exact age, sex, and ethnicity, which has been audited to minimize labeling errors.
Forensic Quality: The images are typically mugshot-style (frontal, controlled lighting, neutral expression), making them ideal for high-precision biometric testing. 3. Key Research Applications
Researchers utilize the Verified MORPH II dataset to solve complex computer vision problems:
Age Estimation: Training deep learning models to predict a person's age from a single photo.
Age-Invariant Face Recognition: Developing algorithms that can recognize a person even if their appearance has changed significantly over a decade.
Demographic Bias Testing: Measuring how face recognition performance varies across different ethnicities and age groups to ensure fairness in AI. 4. Comparison to Other Datasets MORPH II (Verified) Images Subjects Setting Controlled (Mugshots) Uncontrolled (Family photos) In-the-wild (Celebrities) Verification High (Verified metadata) Lower (Web-crawled) 5. Accessibility and Ethics
The dataset is managed by the Face Aging Group at the University of North Carolina Wilmington (UNCW). Access is typically restricted to academic or commercial researchers who must sign a Data Use Agreement (DUA). This ensures the sensitive biometric data is used ethically and prevents the images from being redistributed or used for non-research purposes.
MORPH-II is the second and largest release of the MORPH (Metropolitan Interchange on Reconstructive Progression of High-resolution) project. It contains approximately 55,134 images from 13,618 individuals, with longitudinal spans ranging from a few days to over twenty years.
Demographics: The database includes metadata for age, gender, and ethnicity (primarily European and African, with smaller subsets for Asian and Hispanic).
Applications: It is primarily utilized to address age-related challenges in facial recognition and for training deep learning models in demographic classification. Proposed Subsetting and Verification Schemes
Researchers have proposed various schemes to "verify" and improve the dataset's reliability for training, addressing its inherent racial and gender imbalances:
Independence Schemes: A common verification protocol involves ensuring absolute independence between training and testing sets to prevent "data leakage".
Racial/Gender Balancing: Specific subsetting schemes have been designed to create more uniform distributions, allowing for better generalization in age prediction and race classification tasks.
Synthetic Verification: Newer methods use synthetic face morphing datasets (like the one proposed in 2024 with 2,450 identities) to benchmark against MORPH-II, verifying the vulnerability of face recognition systems to sophisticated morphing attacks. Performance Benchmarks on MORPH-II
MORPH-II serves as a standard benchmark for evaluating the Mean Absolute Error (MAE) and Cumulative Score (CS) of age estimation algorithms.
State-of-the-Art (SOTA): Recent models, such as the Semantic Attention Guided Hierarchical Decision Network, have achieved MAEs as low as 2.18 on this dataset.
Error Rates: Many practical applications consider the dataset "verified" for use when models achieve a CS where roughly 81% of images are predicted with an error of less than 5 years. Key Performance Indicators
If you are asking me to evaluate or write a short argument on the topic:
Short answer:
No, simply stating "Morph II dataset verified — good essay" is not a valid or complete essay. An essay requires a thesis, evidence, analysis, and structure. A single phrase lacks all of these.
If you are proposing an essay topic, a good thesis might be:
"While the Morph II dataset is widely used and has been verified for basic integrity (e.g., no duplicate images, correct subject IDs), its limitations in demographic diversity and controlled capture conditions mean that 'verified' does not automatically make it suitable for all face recognition benchmarks."
To write a good essay on this, you would need to: Diversity : The dataset includes images of people
If you meant something else by your query, please clarify. Are you:
MORPH II dataset (released in 2008) is a foundational longitudinal face database used extensively for research in facial recognition age estimation demographic classification Verified Dataset Overview
The term "verified" in the context of MORPH II typically refers to the 2008 non-commercial release
, which is a cleaned and updated version of the original "MORPHpre" dataset. While widely cited over 500 times, researchers have noted that the raw data (originally sourced from self-reported mugshots) contained inconsistencies that required community-led "cleaning" and verification of metadata like age and race. Total Images : 55,134 unique facial samples. Total Subjects : Approximately 13,000 individuals. : 16 to 77 years. Demographic Balance
: Includes African, European, Asian, and Hispanic subjects, with images balanced across gender and race in specific research protocols. Longitudinal Nature
: Images of the same individuals were captured over multiple years (2003–2007), allowing for research on how aging affects biometric systems. Key Research Applications Age Estimation Protocols
: Researchers use standardized "verified" splits (protocols) to benchmark algorithms for age estimation, ensuring results are comparable across different studies. Morph Attack Detection (MAD)
: MORPH II is a primary source for creating "morphed" face datasets (e.g.,
) to test vulnerabilities in Automated Border Control (ABC) systems where one passport might be used by two look-alike individuals. Demographic Accuracy
: Used to evaluate bias and performance variations across different racial and gender groups in commercial-off-the-shelf (COTS) facial recognition systems. Data Distribution and Folds
For scientific validation, the dataset is often divided into "folds" to ensure a similar distribution of age, gender, and ethnicity in both training and testing sets. Fold Allocation
: All images of a single subject are typically kept within one fold to prevent "identity leakage" (the model recognizing the person rather than learning to estimate age). Subsetting Schemes
: Popular schemes involve balanced subsets, such as 9,600 images equally divided among Black/White Males and Females. How to Access While versions of the dataset exist on platforms like
, the official, verified version for academic use is typically managed through formal research requests to institutions like the University of North Carolina Wilmington (UNCW) to ensure compliance with privacy and ethical standards. specific algorithms
used for age estimation on this dataset or see details on the subsetting protocols AI responses may include mistakes. Learn more arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
A "MORPH II dataset — verified" denotes the MORPH II face-image collection after metadata and identity cleaning, producing more reliable and reproducible data for face recognition and age-related research.
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The MORPH II dataset (Multi-Objective Research Primary Helper) is a premier longitudinal face database widely recognized as a benchmark for facial age estimation, gender classification, and race identification. Developed by the Face Aging Group at the University of North Carolina Wilmington, it is essential for researchers studying how human facial features change over time. Core Dataset Characteristics
MORPH II is significant due to its size and the "longitudinal" nature of its data, meaning it tracks the same individuals across multiple arrest sessions.
Total Samples: It contains approximately 55,134 unique images of about 13,000 subjects. Time Span: Data was collected between 2003 and late 2007.
Demographics: Subjects range in age from 16 to 77 years. The dataset includes diverse ethnic groups, primarily African and European (Black and White), with smaller representations of Hispanic and Asian backgrounds.
Metadata: Each image is accompanied by metadata including age, gender, race, and sometimes physical parameters like BMI. Verification and Cleaning
While widely used, the "verified" status often refers to academic cleaning efforts that have corrected inherent data inconsistencies.
Data Inconsistencies: Initial releases contained errors in self-reported data, such as conflicting birthdates or gender labels for the same subject.
Cleaning Efforts: Notable research has produced "cleaned" versions of the dataset. For instance, the MORPH-II: Inconsistencies and Cleaning Whitepaper details the creation of a "go for age" version, which removes subjects with unidentifiable birthdates to ensure consistent age information for training.
Standard Protocols: Academic researchers often use the 80-20 protocol (80% training, 20% testing) to maintain consistency and allow for fair benchmarking against state-of-the-art models. Research Applications
MORPH II serves as the gold standard for several computer vision tasks:
Facial Age Estimation: Testing models' ability to predict a person's "ground truth" age with low Mean Absolute Error (MAE).
Cross-Age Face Recognition: Investigating how ageing impacts the ability of facial recognition systems to identify a person over decades.
Morphing Attack Detection (MAD): Creating derivative databases (like MorphAge) to study vulnerabilities in face recognition systems when presented with digitally morphed images.
For further detailed statistics, you can access the MORPH Non-Commercial Release Whitepaper provided by the official research team. arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
Before diving into verification, let’s establish the baseline. The MORPH (Longitudinal Morphing) dataset, specifically Album 2 (commonly called MORPH II), was compiled by Karl Ricanek and his team at the University of North Carolina Wilmington. It remains the largest publicly available dataset of its kind designed for facial age progression and estimation.
For researchers building deep learning models to predict age from a selfie or to track how a face changes over time, MORPH II has been the undisputed benchmark.
So, why is the term "verified" attached to this dataset so critical? The raw, unprocessed MORPH II dataset, while invaluable, contains significant noise. When a dataset is not verified, researchers face three core issues:
MORPH II is prized for its demographic diversity. However, unverified noise is often not random—it frequently clusters around minority groups. If verification isn't performed, age labels for African or Hispanic subjects might be systematically noisier than for Caucasians, leading you to falsely conclude your model is biased against those groups (or falsely believe it is fair). Verification ensures that the signal, not the noise, drives demographic analysis.
Longitudinal studies rely on linking images to a unique subject ID. In the unverified dataset, there are documented instances of two different subjects sharing the same ID (collision) or the same subject having multiple IDs (splitting).
The MORPH II dataset (often referred to simply as MORPH) is one of the most widely cited and influential datasets in the fields of computer vision, biometrics, and automated age estimation. Created by Karl Ricanek Jr. and his team at the University of North Carolina Wilmington (UNCW), it was designed to address a significant gap in facial aging research: the lack of a large-scale, longitudinal dataset containing real-world, unconstrained facial images.
Unlike laboratory-controlled datasets (e.g., FERET, FG-NET), MORPH II comprises images collected from actual mug shot booking systems. As of its final release (Album 2, released around 2007–2008), MORPH II contains approximately 55,000+ images from over 13,000 subjects, with ages ranging from 16 to 77 years. Each subject has multiple images (an average of ~4 images per person) captured over a span of weeks to years, allowing for the modeling of intra-subject facial aging.
Key characteristics:
When researchers and practitioners refer to "MORPH II dataset verified," they are almost always talking about label verification—specifically, the verification of the age labels attached to each facial image. This is not about verifying the identity of the subject (though that is implicit) but about ensuring that the recorded age is accurate and reliable for training supervised learning models.