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{{short description|An overview of the Iris dataset used in statistical classification and machine learning.}} | |||
== | ==Iris dataset== | ||
[[File:Iris_dataset_scatterplot.svg|thumb|right|Scatterplot of the Iris dataset showing the relationship between different features.]] | |||
The '''Iris dataset''' is a classic and widely used dataset in the field of [[statistics]] and [[machine learning]]. It is often used as a beginner's dataset for demonstrating various [[classification]] algorithms and techniques. The dataset contains 150 samples of iris flowers, each described by four features: [[sepal length]], [[sepal width]], [[petal length]], and [[petal width]]. | |||
A | ===History=== | ||
The Iris dataset was introduced by the British biologist and statistician [[Ronald A. Fisher]] in 1936. It was part of his work on [[discriminant analysis]], a statistical technique used to distinguish between different sets of data. Fisher's work laid the foundation for many modern statistical methods and machine learning algorithms. | |||
== | ===Structure=== | ||
The dataset consists of 150 samples from three species of iris flowers: [[Iris setosa]], [[Iris versicolor]], and [[Iris virginica]]. Each species is represented by 50 samples. The four features measured for each sample are: | |||
* Sepal length in centimeters | |||
* Sepal width in centimeters | |||
* Petal length in centimeters | |||
* Petal width in centimeters | |||
These features are used to classify the samples into one of the three species. | |||
===Applications=== | |||
The Iris dataset is commonly used in [[machine learning]] for: | |||
* | * [[Supervised learning]]: It is used to train and test classification algorithms such as [[k-nearest neighbors]], [[support vector machines]], and [[decision trees]]. | ||
* [[Data visualization]]: The dataset is often used to demonstrate techniques such as [[scatter plots]], [[histograms]], and [[box plots]]. | |||
* [[Dimensionality reduction]]: Techniques like [[principal component analysis]] (PCA) are applied to the dataset to reduce the number of features while preserving the variance. | |||
===Challenges=== | |||
While the Iris dataset is a useful tool for learning and experimentation, it has limitations: | |||
* It is a small dataset, which may not be representative of real-world data. | |||
* The classes are linearly separable, which may not be the case in more complex datasets. | |||
==Related pages== | |||
* [[Machine learning]] | |||
* [[Statistical classification]] | |||
* [[Ronald A. Fisher]] | |||
* [[Discriminant analysis]] | |||
[[Category:Datasets]] | |||
[[Category:Machine learning]] | |||
[[Category: | |||
[[Category: | |||
[[Category:Statistics]] | [[Category:Statistics]] | ||
Latest revision as of 11:12, 15 February 2025
An overview of the Iris dataset used in statistical classification and machine learning.
Iris dataset[edit]
The Iris dataset is a classic and widely used dataset in the field of statistics and machine learning. It is often used as a beginner's dataset for demonstrating various classification algorithms and techniques. The dataset contains 150 samples of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width.
History[edit]
The Iris dataset was introduced by the British biologist and statistician Ronald A. Fisher in 1936. It was part of his work on discriminant analysis, a statistical technique used to distinguish between different sets of data. Fisher's work laid the foundation for many modern statistical methods and machine learning algorithms.
Structure[edit]
The dataset consists of 150 samples from three species of iris flowers: Iris setosa, Iris versicolor, and Iris virginica. Each species is represented by 50 samples. The four features measured for each sample are:
- Sepal length in centimeters
- Sepal width in centimeters
- Petal length in centimeters
- Petal width in centimeters
These features are used to classify the samples into one of the three species.
Applications[edit]
The Iris dataset is commonly used in machine learning for:
- Supervised learning: It is used to train and test classification algorithms such as k-nearest neighbors, support vector machines, and decision trees.
- Data visualization: The dataset is often used to demonstrate techniques such as scatter plots, histograms, and box plots.
- Dimensionality reduction: Techniques like principal component analysis (PCA) are applied to the dataset to reduce the number of features while preserving the variance.
Challenges[edit]
While the Iris dataset is a useful tool for learning and experimentation, it has limitations:
- It is a small dataset, which may not be representative of real-world data.
- The classes are linearly separable, which may not be the case in more complex datasets.
