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Scatterplot of the data set. 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 in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. [1]
An example of a decision stump that discriminates between two of three classes of Iris flower data set: Iris versicolor and Iris virginica. The petal width is in centimetres. This particular stump achieves 94% accuracy on the Iris dataset for these two classes. A decision stump is a machine learning model consisting of a one-level decision tree ...
English: The scatterplot of Iris flower data set, collected by Edgar Anderson and popularized in the Machine learning community by Ronald Fisher. Español: Diagrama de dispersión del conjunto de datos de la flor Iris , recolectada por Edgar Anderson y popularizada en la comunidad de aprendizaje automático por Ronald Fisher .
In 1936, he introduced the Iris flower data set as an example of discriminant analysis. [66] In his 1937 paper The wave of advance of advantageous genes he proposed Fisher's equation in the context of population dynamics to describe the spatial spread of an advantageous allele, and explored its travelling wave solutions. [67]
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Deutsch: Iris flower data set, mit dem k-Means-Algorithmus analysiert (links) und die wahren Spezien im Datensatz (rechts). Da k-means nicht deterministisch ist, variieren die Ergebnisse. Die Clusterzentren sind durch größere, halbtransparente Markierungen eingezeichnet.
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Iris douglasiana, the Douglas iris, is a common wildflower of the coastal regions of Northern and Central California and southern Oregon in the United States. [2] It grows mainly at lower elevations, below 100 meters (330 ft), though it is occasionally found at heights of up to 1,000 meters (3,300 ft).