The Iris Classification dataset is a well-known dataset in machine learning, commonly used for classification tasks. It contains measurements of various iris flowers, including sepal length, sepal width, petal length, and petal width, as well as the corresponding species label.

Columns Description:

1. Sepal Length (cm): Length of the sepals of the iris flower.

2. Sepal Width (cm): Width of the sepals of the iris flower.

3. Petal Length (cm): Length of the petals of the iris flower.

4. Petal Width (cm): Width of the petals of the iris flower.

5. Species: The species of the iris flower, which is the target variable to be predicted.

This dataset is commonly used for practicing classification algorithms and exploring data analysis techniques.

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

Iris Flower Dataset

This is a classic and very widely used dataset in machine learning and statistics, often serving as a first dataset for classification problems. Introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems," it is a foundational resource for learning classification algorithms.

Overview:

The dataset contains measurements for 150 samples of iris flowers. Each sample belongs to one of three species of iris:

• Iris setosa
• Iris versicolor
• Iris virginica

For each flower, four features were measured:

• Sepal length (in cm)
• Sepal width (in cm)
• Petal length (in cm)
• Petal width (in cm)

The goal is typically to build a model that can classify iris flowers into their correct species based on these four features.

File Structure:

The dataset is usually provided as a single CSV (Comma Separated Values) file, often named iris.csv or similar. This file typically contains the following columns:

1. sepal_length (cm): Numerical. The length of the sepal of the iris flower.
2. sepal_width (cm): Numerical. The width of the sepal of the iris flower.
3. petal_length (cm): Numerical. The length of the petal of the iris flower.
4. petal_width (cm): Numerical. The width of the petal of the iris flower.
5. species: Categorical. The species of the iris flower (either 'setosa', 'versicolor', or 'virginica'). This is the target variable for classification.

Content of the Data:

The dataset contains an equal number of samples (50) for each of the three iris species. The measurements of the sepal and petal dimensions vary between the species, allowing for their differentiation using machine learning models.

How to Use This Dataset:

1. Download the iris.csv file.
2. Load the data using libraries like Pandas in Python.
3. Explore the data through visualization and statistical analysis to understand the relationships between the features and the different species.
4. Build classification models (e.g., Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors) using the sepal and petal measurements as features and the 'species' column as the target variable.
5. Evaluate the performance of your model using appropriate metrics (e.g., accuracy, precision, recall, F1-score).
6. The dataset is small and well-behaved, making it excellent for learning and experimenting with various classification techniques.

Citation:

When using the Iris dataset, it is common to cite Ronald Fisher's original work:

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188.

Data Contribution:

Thank you for providing this classic and fundamental dataset to the Kaggle community. The Iris dataset remains an invaluable resource for both beginners learning the basics of classification and experienced practitioners testing new algorithms. Its simplicity and clear class separation make it an ideal starting point for many data science projects.

If you find this dataset description helpful and the dataset itself useful for your learning or projects, please consider giving it an upvote after downloading. Your appreciation is valuable!

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 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.

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

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.

Iris Species Dataset

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 dataset is taken from UCI Machine Learning Repository's… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/iris.

The Iris dataset is a classic and widely used dataset in machine learning for classification tasks. It consists of measurements of different iris flowers, including sepal length, sepal width, petal length, and petal width, along with their corresponding species. With a total of 150 samples, the dataset is balanced and serves as an excellent choice for understanding and implementing classification algorithms. This notebook explores the dataset, preprocesses the data, builds a decision tree classification model, and evaluates its performance, showcasing the effectiveness of decision trees in solving classification problems.

The simple Iris dataset for Multiclass Classification (required dataset for Hello World program in Machine Learning). It has data for three species (Iris-setosa, Iris-versicolor and Iris-virginica) of Iris flower. It contains 4 features as input (petal_length, petal-width, sepal_length and sepal_width) and label as output. It contains 150 rows, 50 rows for each species of Iris.

This research proposes a method to detect alcohol consumption from Near-Infra-Red (NIR) periocular eye images. The study focuses on determining the effect of external factors such as alcohol on the Central Nervous System (CNS). The goal is to analyse how this impacts on iris and pupil movements and if it is possible to capture these changes with a standard iris NIR camera. This paper proposes a novel Fused Capsule Network (F-CapsNet) to classify iris NIR images taken under alcohol consumption subjects.

language:

"en" pretty_name: "Iris Dataset" tags: tabular-data classification machine-learning license: "public-domain" task_categories: classification task_ids: multi-class-classification size_categories: small source_datasets: original annotations_creators: expert-generated dataset_creators: Edgar Anderson paperswithcode_id: iris multilinguality: "monolingual"

This project uses the Iris dataset from the RDatasets Julia package. It consists of 150 flower samples equally distributed across three species: Setosa, Versicolor, and Virginica. Each sample includes four numerical features: sepal length, sepal width, petal length, and petal width. The features are normalized for model input. The dataset is split into 80% training and 20% testing to evaluate a neural network model developed using Flux.jl for accurate species classification.

Previous studies have shown that light iris color is a predisposing factor for the development of uveal melanoma (UM) in a population of Caucasian ancestry. However, in all these studies, a remarkably low percentage of patients have brown eyes, so we applied deep learning methods to investigate the correlation between iris color and the prevalence of UM in the Chinese population. All anterior segment photos were automatically segmented with U-NET, and only the iris regions were retained. Then the iris was analyzed with machine learning methods (random forests and convolutional neural networks) to obtain the corresponding iris color spectra (classification probability). We obtained satisfactory segmentation results with high consistency with those from experts. The iris color spectrum is consistent with the raters’ view, but there is no significant correlation with UM incidence.

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

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.

This is a simulated version of the classic Iris dataset, commonly used for machine learning and data analysis practice. It includes measurements for sepal length, sepal width, petal length, and petal width, along with species classification. Ideal for beginners learning data science and machine learning.

ObjectivesIn veterinary medicine, attempts to apply artificial intelligence (AI) to ultrasonography have rarely been reported, and few studies have investigated the value of AI in ultrasonographic diagnosis. This study aimed to develop a deep learning-based model for classifying the status of canine chronic kidney disease (CKD) using renal ultrasonographic images and assess its diagnostic performance in comparison with that of veterinary imaging specialists, thereby verifying its clinical utility.Materials and methodsIn this study, 883 ultrasonograms were obtained from 198 dogs, including those diagnosed with CKD according to the International Renal Interest Society (IRIS) guidelines and healthy dogs. After preprocessing and labeling each image with its corresponding IRIS stage, the renal regions were extracted and classified based on the IRIS stage using the convolutional neural network-based object detection algorithm You Only Look Once. The training scenarios consisted of multi-class classification, categorization of images into IRIS stages, and four binary classifications based on specific IRIS stages. To prevent model overfitting, we balanced the dataset, implemented early stopping, used lightweight models, and applied dropout techniques. Model performance was assessed using accuracy, recall, precision, F1 score, and receiver operating characteristic curve and compared with the diagnostic accuracy of four specialists. Inter- and intra-observer variabilities among specialists were also evaluated.ResultsThe developed model exhibited a low accuracy of 0.46 in multi-class classification. However, a significant performance improvement was observed in binary classifications, with the model designed to distinguish stage 3 or higher showing the highest accuracy of 0.85. In this classification, recall, precision, and F1 score values were all 0.85, and the area under the curve was 0.89. Compared with radiologists, whose accuracy ranged from 0.48 to 0.62 in this experimental scenario, the AI model exhibited superiority. Intra-observer reliability among radiologists was substantial, whereas inter-observer variability showed a moderate level of agreement.ConclusionsThis study developed a deep-learning framework capable of reliably classifying CKD IRIS stages 3 and 4 in dogs using ultrasonograms. The developed framework demonstrated higher accuracy than veterinary imaging specialists and provided more objective and consistent interpretations. Therefore, deep-learning-based ultrasound diagnostics are potentially valuable tools for diagnosing CKD in dogs.

Nous verrons dans ce tutoriel comment détecter des modèles à l’aide du classificateur bayésien naïf, une technique d’apprentissage-machine efficace pour détecter certains modèles et prévoir les dépendances au sein de votre jeu de données. Nous réexaminerons dans la première partie de ce tutoriel le jeu de données Iris utilisé dans le tutoriel précédent pour apprendre à utiliser le classificateur bayésien naïf. Nous appliquerons par la suite vos nouvelles connaissances pour déceler les pourriels parmi vos messages textes (SMS), de manière à identifier les messages que vous ne désirerez pas lire. Le jeu de données que nous utiliserons s’agit d’un jeu de données de source libre du Référentiel d’apprentissage-machine UCI. Nous examinerons ensuite la classification multi-étiquettes via le jeu de données CMU que nous avons utilisé antérieurement pour le classificateur des plus proches voisins. Enfin, nous vous donnerons un exemple d’utilisation non aboutie du classificateur bayésien et vous expliquerons pourquoi cela n’a pas fonctionné. The tutorial revisits the Iris flower dataset to introduce the basic steps of working with the Naive Bayes Classifier. It then applies the classifier to detect spam in SMS messages using the SMS Spam collection dataset from the UCI Machine Learning Repository, and performs multi-label classification using the CMU book dataset. The tutorial also presents a scenario where the Naive Bayes Classifier fails, providing an explanation for the failure. By the end of this tutorial, participants will have a solid understanding of the Naive Bayes classifier, be able to split data into training and testing sets, make predictions, evaluate classifier performance, identify spam, classify books, train a Gaussian Naive Bayes classifier for single or multiple labels, and utilize imputation techniques for handling missing data.