Context

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.

Content

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 Sepal Width vs. Sepal Length

Analysis of ‘Iris Flower Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arshid/iris-flower-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. The data set consists of 50 samples from each of three species of Iris (Iris Setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.

This dataset became a typical test case for many statistical classification techniques in machine learning such as support vector machines

Content

The dataset contains a set of 150 records under 5 attributes - Petal Length, Petal Width, Sepal Length, Sepal width and Class(Species).

Acknowledgements

This dataset is free and is publicly available at the UCI Machine Learning Repository

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Md Raiesh

Contents

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.

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

Context: 🌼 The Iris flower dataset, an iconic multivariate set, was first introduced by the renowned British statistician and biologist, Ronald Fisher in 1936 📝. Commonly known as Anderson's Iris dataset, it was curated by Edgar Anderson to measure the morphologic variation of three Iris species 🌸: Iris Setosa, Iris Virginica, and Iris Versicolor.

📊 The set comprises 100 samples from each species, with four features - sepal length, sepal width, petal length, and petal width, measured in centimetres.

🔬 This dataset has since served as a standard test case for various statistical classification techniques in machine learning, including the widely used support vector machines (SVM).

So, whether you're a newbie dipping your toes into the ML pond or a seasoned data scientist testing out a new classification method, the Iris dataset is a classic starting point! 🎯🚀

Columns:

1. Sepal Length: The length of the sepal of the iris flower, is measured in centimetres.
2. Sepal Width: The width of the sepal of the iris flower, measured in centimetres.
3. Petal Length: The length of the petal of the iris flower, is measured in centimetres.
4. Petal Width: The width of the petal of the iris flower, measured in centimetres.
5. Species:The specific species of the iris flower, categorized into Sentosa, Virginica, and Versicolor.

Problem Statement:

1.🎯 Classification Challenge: Can you accurately predict the species of an Iris flower based on the four given measurements: sepal length, sepal width, petal length, and petal width?

2.💡 Feature Importance: Which feature (sepal length, sepal width, petal length, or petal width) is the most significant in distinguishing between the species of Iris flowers?

3.📈 Data Scaling: How does standardization (or normalization) of the features affect the performance of your classification models?

4.🧪 Model Experimentation: Can simpler models such as Logistic Regression perform as well as more complex models like Support Vector Machines or Neural Networks on the Iris dataset? Compare the performance of various models.

5.🤖 AutoML Challenge: Use AutoML tools (like Google's AutoML or H2O's AutoML) to build a classification model. How does its performance compare with your handcrafted models?

Kindly, upvote if you find the dataset interesting

The Iris flower data set or Fisher’s Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. The dataset was introduced in his 1936 paper "The Use of Multiple Measurements in Taxonomic Problems" (Fisher 1936) as an example of linear discriminant analysis. This dataset has the following Features: Petal.Length: Length of the petal Petal.Width: Width of the petal Sepal.Length: Length of the sepal Sepal.Width: Width of the sepal It has a total of 3 Groups: setosa, versicolor, and virginica. {"references": ["H\u00f6nel, Sebastian, Morgan Ericsson, Welf L\u00f6we, and Anna Wingkvist. 2022. "Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?" In 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022, Guangzhou, China, December 5-9, 2022, 333\u201343. IEEE. https://doi.org/10.1109/QRS57517.2022.00042.", "Fisher, R. A. 1936. "The Use of Multiple Measurements in Taxonomic Problems." Annals of Eugenics 7 (2): 179\u201388. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x."]}

Dataset Card for Iris Dataset

  Dataset Summary

The Iris dataset is a classic multivariate dataset introduced by Ronald Fisher in 1936. It contains 150 samples of iris flowers from three different species: Iris setosa, Iris versicolor, and Iris virginica. Each sample has four features: sepal length, sepal width, petal length, and petal width. This dataset is widely used for classification tasks, especially in machine learning tutorials and benchmarks.

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/mariaasoriaano/iris_clase.

Dataset Card for "iris"

  Dataset Summary

The Iris dataset is one of the most classic datasets in machine learning, often used for classification and clustering tasks. It contains 150 samples of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width. The task is to classify the samples into one of three species: Iris setosa, Iris versicolor, or Iris virginica. This dataset is especially useful for:

Supervised learning… See the full description on the dataset page: https://huggingface.co/datasets/aegarciaherrera/iris-clase.

This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

1. Title: Iris Plants Database

2. Sources: (a) Creator: R.A. Fisher (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) (c) Date: July, 1988

3. Past Usage:

   • Publications: too many to mention!!! Here are a few.
   • Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950).
   • Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
   • Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71. -- Results: -- very low misclassification rates (0% for the setosa class)
   • Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433. -- Results: -- very low misclassification rates again
   • See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II conceptual clustering system finds 3 classes in the data.

4. Relevant Information: --- 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. --- Predicted attribute: class of iris plant. --- This is an exceedingly simple domain. --- This data differs from the data presented in Fishers article

5. Number of Instances: 150 (50 in each of three classes)

6. Number of Attributes: 4 numeric, predictive attributes and the class

7. Attribute Information:

   1. sepal length in cm
   2. sepal width in cm
   3. petal length in cm
   4. petal width in cm
   5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

8. Missing Attribute Values: None

Summary Statistics:

sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)

1. Class Distribution: 33.3% for each of 3 classes.

The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. The data set consists of 50 samples from each of three species of Iris (Iris Setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.This dataset became a typical test case for many statistical classification techniques in machine learning such as support vector machines

Partitioned IRIS Datasets

This repository contains a script (dataset.py) to download the Iris dataset and split it into multiple partitions. Each partition is further divided into a public "mock" dataset and a "private" dataset.

  IRIS Dataset Overview

The Iris dataset is a classic dataset in machine learning, consisting of 150 samples of iris flowers. Each sample has four features (sepal length, sepal width, petal length, and petal width) and belongs to one of three… See the full description on the dataset page: https://huggingface.co/datasets/khoaguin/iris-partitions.

The Iris dataset is a classic dataset in the field of machine learning, containing measurements of various features of iris flowers, such as sepal length, sepal width, petal length, and petal width, along with their corresponding species. Through analysis, we aim to explore the characteristics of different iris species, identify patterns in their measurements, and potentially build predictive models to classify iris species based on their features. This dataset serves as an excellent resource for understanding and practicing classification techniques in data science.

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.

Analysis of ‘Iris Dataset for EDA’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mdjafrilalamshihab/iris-dataset-for-eda on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Iris dataset for EDA. This dataset consists petal length and width , sepal length and width and name of species.

--- Original source retains full ownership of the source dataset ---

Iris Petal and Sepal Dataset Description The Iris dataset is one of the most famous datasets in the field of machine learning and statistical classification. It was first introduced by British biologist and statistician Ronald Fisher in 1936 as an example of linear discriminant analysis. The dataset is widely used for educational purposes and model building in machine learning due to its simplicity and versatility.

Dataset Overview The dataset contains 150 observations of Iris flowers from three species:

Iris Setosa Iris Versicolor Iris Virginica Each observation includes four numerical features:

Sepal Length (cm) Sepal Width (cm) Petal Length (cm) Petal Width (cm) Additionally, the dataset provides a class label for the species of the Iris flower.

Feature Descriptions: Sepal Length: The length of the flower’s sepal in centimeters. Sepal Width: The width of the flower’s sepal in centimeters. Petal Length: The length of the flower’s petal in centimeters. Petal Width: The width of the flower’s petal in centimeters. Species: The class label that classifies the flower into one of three species (Setosa, Versicolor, Virginica). Data Summary: 150 instances (50 samples per species) 4 features (numeric data) 1 target variable (categorical – species of the flower) Applications: The dataset is often used for:

Classification tasks: Building models to classify the species of Iris flowers. Exploratory data analysis (EDA): Exploring relationships between features. Data visualization: Plotting petal and sepal dimensions to understand patterns. Predictive modeling: Training and testing machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), and decision trees. Why This Dataset? The Iris dataset is ideal for beginners and experts alike, as it provides an easy introduction to supervised learning. It is perfect for understanding basic classification algorithms and exploring key concepts such as:

Multiclass classification Feature correlation Data visualization techniques This description is tailored for the Kaggle community and provides a clear overview of the dataset’s content and potential use cases. You can customize it further if needed!

Iris Flower Classification is a classic machine learning task used for learning and practicing classification algorithms. The dataset contains features like sepal length, sepal width, petal length, and petal width for three different species of iris flowers. This project involves data pre-processing, model selection, and evaluation. Here, we use classification algorithms like logistic regression, decision trees, k-nearest neighbors (KNN), or support vector machines (SVM) for this classification task.

Premise: Genetic variation influences potential for evolution to rescue populations from impacts of environmental change. Most studies of genetic variation in fitness-related traits focus on either vegetative or floral traits, with few on floral scent. How vegetative and floral traits compare in potential for adaptive evolution is poorly understood. Results: Vegetative traits SLA and WUE varied greatly among planting sites, while showing weak or no genetic variation among source populations. SLA and trichomes responded plastically to snowmelt date, and SLA exhibited within-population genetic variation. All aspects of floral morphology varied genetically among source populations, and corolla length, corolla width, and sepal width varied genetically within populations. Heritability was not detected for volatiles, due to high environmental variation, although one terpene had high evolvability and two terpenes correlated genetically with sepal width, associated with high emission from that tissue. Environmental variation across sites was weak for floral morphology and stronger for volatiles and vegetative traits. Three of 4 volatiles showed inheritance departing from additive. Conclusions: Results indicate stronger genetic potential for evolutionary responses to selection in floral morphology compared with scent and vegetative traits, while finding potentially adaptive plasticity in some vegetative traits. Methods Genetic families of seeds were produced using reciprocal factorial crosses and planted into two field sites in 2007 and 2008 as seed. Traits were measured between 2009 and 2018 on these plants. Leaves were collected for measurements of specific leaf area and trichome density. A portable photosynthesis instrument (Licor 6400) was used to measure intrinsic water-use efficiency. Floral morphometrics were measured with calipers. Nectar production was measured by covering flowers to prevent access by pollinators and using microcapillary tubes to extract nectar. Floral color was measured using reflectance spectrometry. Floral scents were sampled with dynamic headspace sampling and processed using thermal desorption gas chromatograpy - mass spectrometry.

Abundance and visitation of pollinator assemblages tend to decrease with altitude, leading to an increase in pollen limitation. Thus increased competition for pollinators may generate stronger selection on attractive traits of flowers at high elevations and cause floral adaptive evolution. Few studies have related geographically variable selection from pollinators and intraspecific floral differentiation. We investigated the variation of Trollius ranunculoides flowers and its pollinators along an altitudinal gradient on the eastern Qinghai-Tibet Plateau, and measured phenotypic selection by pollinators on floral traits across populations. The results showed significant decline of visitation rate of bees along altitudinal gradients, while flies was unchanged. When fitness is estimated by the visitation rate rather than the seed number per plant, phenotypic selection on the sepal length and width shows a significant correlation between the selection strength and the altitude, with stronger selection at higher altitudes. However, significant decreases in the sepal length and width of T. ranunculoides along the altitudinal gradient did not correspond to stronger selection of pollinators. In contrast to the pollinator visitation, mean annual precipitation negatively affected the sepal length and width, and contributed more to geographical variation in measured floral traits than the visitation rate of pollinators. Therefore, the sepal size may have been influenced by conflicting selection pressures from biotic and abiotic selective agents. This study supports the hypothesis that lower pollinator availability at high altitude can intensify selection on flower attractive traits, but abiotic selection is preventing a response to selection from pollinators.