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Iris Species Identification.pdf

IRJET-V10I175.pdf
Preview of Iris Species Identification
🔗 Source: irjet.net
📊 Size: 4.92 MB
👤 Author: sony
⬇️ Downloads: 104

Summary

The study uses machine learning algorithms to identify different species of iris flowers, specifically Versicolor, Setosa, and Virginica, using the Iris dataset with 50 occurrences of each class. The algorithms used include neural networks, logistic regression, k-Nearest Neighbors, decision trees, Linear Support Vector Classifiers, random forest classifiers, and Gaussian Naive Bayes. The goal is to classify iris flowers based on their characteristics, such as sepal length, sepal width, petal length, and petal width. The study uses a supervised ML-based method, classification, to determine the group to which data instances belong, and the Scikit Learn Tool Kit to simplify classification.

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Description

The study uses machine learning algorithms to identify different species of iris flowers, specifically Versicolor, Setosa, and Virginica, using the Iris...

Technical Information

  • File Format: PDF
  • File Size: 4.92 MB
  • Pages: 5
  • Language: EN
  • Author: sony
  • Total Downloads: 104
  • Last Updated: 1 week ago

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This PDF document about Iris Species Identification provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Iris Species Identification.

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