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Iris Flower Tidy Classification Data
Data Science and Analytics
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Free
About
Provides fundamental morphological measurements necessary for the classification of Iris flower species. It is a key tool in educational settings for teaching and testing supervised learning algorithms, such as decision trees, and is frequently used for model comparison and benchmarking.
Columns
The dataset contains five fields across 600 records. All data points are recorded as valid, with zero missing or mismatched entries reported.
- Species: Identifies the classification of the Iris flower. This column features three unique values, with 'setosa' being the most common category, accounting for 33% of the records.
- Part: Specifies the measured structure of the flower, which is either the petal or the sepal. This field has two unique values, with 'Sepal' being recorded 50% of the time.
- Measure: Indicates the dimension being measured: length or width. It contains two unique values, with 'Length' being the most frequent measure at 50%.
- Value: Represents the actual measurement in millimeters. The values range from a minimum of 0.1 mm up to a maximum of 7.9 mm. The average measurement value is 3.46 mm.
Distribution
The data file is provided in CSV format, specifically named
iris_tidy.csv. The file size is 23.09 kB and contains 600 individual records. The data quality is high, with 100% validity across all columns.Usage
With a perfect usability score of 10.00, this data set is ideally suited for academic and technical applications. It is often employed for constructing decision tree algorithms, performing detailed model comparison, and demonstrating linear discriminant analysis in taxonomic problem-solving scenarios.
Coverage
This resource covers biological measurements related to the morphological characteristics of Iris flowers. Given its historical nature (introduced in 1936), the expected update frequency is listed as "Never". The topical scope falls under Earth and Nature. Specific geographic or time ranges are not detailed in the provided materials.
License
CC0: Public Domain
Who Can Use It
The primary audience includes educators and students learning classification algorithms, data scientists engaged in model comparison and benchmarking, and statisticians exploring foundational taxonomic problems using linear methods.
Dataset Name Suggestions
- Iris Flower Tidy Classification Data
- Fisher's 1936 Iris Measurements
- Cleaned Iris Taxonomy Data
- Linear Discriminant Analysis Example Set
Attributes
Original Data Source: Iris Flower Tidy Classification Data
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