The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

The columns in this dataset are:

• Id
• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

The Iris Dataset consists of 150 iris samples, each having four numerical features: sepal length, sepal width, petal length, and petal width. Each sample is categorized into one of three iris species: Setosa, Versicolor, or Virginica. This dataset is widely used as a sample dataset in machine learning and statistics due to its simple and easily understandable structure.

Feature Information : - Sepal Length (cm) - Sepal Width (cm) - Petal Length (cm) - Petal Width (cm)

Target Information : - Iris Species : 1. Setosa 1. Versicolor 1. Virginica

Source : The Iris Dataset is obtained from the scikit-learn (sklearn) library under the BSD (Berkeley Software Distribution) license.

File Formats :

1. CSV (Comma-Separated Values): CSV format is the most commonly used and easily readable format. Each row represents one sample with its features separated by commas.
2. Excel (.xlsx): Excel format is suitable for further data analysis, visualization, and integration with other software.
3. JSON (JavaScript Object Notation): JSON format allows data to be stored in a more complex structure, suitable for web-based data processing or applications.
4. Parquet: Parquet format is an efficient columnar data format for large and complex data.
5. HDF5 (Hierarchical Data Format version 5): HDF5 format stores data in hierarchical groups and datasets, excellent for storing large scientific and numerical data.
6. Feather: Feather format is a lightweight binary format for storing data frames. It provides excellent performance for reading and writing data.
7. SQLite Database (.db, .sqlite): SQLite is a lightweight database format suitable for local data storage and querying. It is widely used for small to medium-scale applications.
8. Msgpack: Msgpack format is a binary serialization format that is efficient in terms of storage and speed. It is suitable for storing and transmitting data efficiently between systems.

The Iris Dataset is one of the most iconic datasets in the world of machine learning and data science. This dataset contains information about three species of iris flowers: Setosa, Versicolor, and Virginica. With features like sepal and petal length and width, the Iris dataset has been a stepping stone for many beginners in understanding the fundamental concepts of classification and data analysis. With its clarity and diversity of features, the Iris dataset is perfect for exploring various machine learning techniques and building accurate classification models. I present the Iris dataset from scikit-learn with the hope of providing an enjoyable and inspiring learning experience for the Kaggle community!

EPA?s Integrated Risk Information System (IRIS) is a compilation of electronic reports on specific substances found in the environment and their potential to cause human health effects.

Iris dataset

"Exploring Patterns: The Iris Dataset Analysis" https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19517213%2F157356294f32ae95956a495960b7ea39%2F1_ZK9_HrpP_lhSzTq9xVJUQw.png?generation=1711814010788837&alt=media" alt=""> The Iris dataset is a classic dataset in the field of machine learning and statistics. It was introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems" as an example of linear discriminant analysis. The dataset consists of 150 samples of iris flowers, each belonging to one of three species: Setosa, Versicolor, and Virginica.

Columns : -

• Sepal Length (cm):The length of the sepal, which is the outermost part of the flower that protects the petals.
• Sepal Width (cm): The width of the sepal, measured at its widest point.
• Petal Length (cm): The length of the petal, which is the colorful inner part of the flower.
• Petal Width (cm): The width of the petal, measured at its widest point.
• Species: The species of iris flower, which can be one of three classes: Setosa, Versicolor, or Virginica.

These columns provide measurements of various parts of the iris flower, along with the corresponding species labels, making it a versatile dataset for analysis and classification tasks.

This is an iris dataset commonly used in machine learning. Accessed on 10-19-2020 from the following URL: http://faculty.smu.edu/tfomby/eco5385_eco6380/data/Iris.xls

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('iris', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

IRIS tracks the status of safety deficiencies identified during Occupational Safety and Health Administration (OSHA) and Safety & Environmental Management (SEM) survey inspections.

Fisher's Iris dataset is a multivariate dataset introduced by Sir Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems". It contains 150 samples from three species of iris flowers (Iris setosa, Iris virginica, and Iris versicolor). Each sample is described by 4 features: the length and width of the sepal and petal.

Note

The Iris dataset is one of the most popular datasets used for demonstrating simple classification models. This dataset was copied and transformed from scikit-learn/iris to be more native to huggingface. Some changes were made to the dataset to save the user from extra lines of data transformation code, notably:

removed id column species column is casted to ClassLabel (supports ClassLabel.int2str() and ClassLabel.str2int()) cast feature columns from float64 down to float32… See the full description on the dataset page: https://huggingface.co/datasets/hitorilabs/iris.

IRIS Work Item Module supports Real Property Asset Management (RPAM) and the Financial Operation Division. IRIS manages the estimated cost of building projects related to repairs and alterations, and new construction

This dataset present a unique collection of iris imaes of African descent. It is the first publicly available human iris datasets of African descents. Three categories of images were collected from 1028 volunteers that participated in the data collection task. The first category is made up of four iris images that were captured when the volunteers used spectacles while the second category includes four sets of iris images captured when the volunteers are without spectacles. The third category is iris images obtained from volunteers that used lenses.

Iris Dataset

The classic Iris dataset in .parquet format. Useful for ML demos, classification tasks, and model testing.

BIT/iris-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Iris dataset in json format

Iris Dataset

BrianSuToronto/iris-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

1) Data Introduction • The Iris Species Dataset is a classic multi-class classification data that collected a total of 150 samples, 50 for each of the three iris species (Setosa, Versicolor, Virginica), consisting of four numerical characteristics and species labels, including calyx length, width, petal length, and width.

2) Data Utilization (1) The Iris Species Dataset has characteristics that: • This dataset consists of a total of six columns and is labeled as one of three types, making it suitable for class division and basic statistical analysis. (2) The Iris Species Dataset can be used to: • Classification Algorithm Practice: You can easily practice various machine learning classification models such as logistic regression, SVM, and decision tree by inputting four characteristics: calyx and petal length and width. • Visualize data and analyze basic statistics: Visualize the distribution of characteristics by variety into scatterplots, boxplots, etc. to explore differences between classes and correlations between characteristics.

Iris Species Dataset

The Iris dataset is a classic dataset in machine learning, originally published by Ronald Fisher. It contains 150 instances of iris flowers, each described by four features (sepal length, sepal width, petal length, and petal width), along with the corresponding species label (setosa, versicolor, or virginica). It is commonly used as an introductory dataset for classification tasks and for demonstrating basic data exploration and model training workflows.… See the full description on the dataset page: https://huggingface.co/datasets/brjapon/iris.

CASIA-Iris-Thousand contains 20,000 iris images from 1,000 subjects, which were collected using IKEMB-100 camera (Fig. 8) produced by IrisKing. IKEMB-100 is a dual-eye iris camera with friendly visual feedback, realizing the effect of “What You See Is What You Get”. The bounding boxes shown in the frontal LCD help users adjust their pose for high-quality iris image acquisition. The main sources of intra-class variations in CASIA-Iris-Thousand are eyeglasses and specular reflections. Since CASIA-Iris-Thousand is the first publicly available iris dataset with one thousand subjects, it is well-suited for studying the uniqueness of iris features and develop novel iris classification and indexing methods.

The CASIA Iris Syn dataset introduces diverse intra-class variations including deformation, blurring, and rotation, making it a robust resource for research in iris recognition, biometric security, and AI-based feature extraction.