IRIS Flower Classification using SKLEARN Random Forest Classifier with Grid Search Cross Validation
The IRIS flower is a popular example in the field of machine learning. It is a type of flower that has different variations, such as the setosa, virginica, and versicolor. In this blog, we will be discussing how to classify the IRIS flower using a machine learning technique called Random Forest Classifier and a method called Grid Search Cross Validation.
A Random Forest Classifier is a type of algorithm that is used to classify items into different categories. It works by creating multiple decision trees and combining their results to make a final classification. This method is called ensemble learning, where multiple models are used to improve the overall performance.
Grid Search Cross Validation is a method used to find the best set of parameters for the Random Forest Classifier. It works by testing different combinations of parameters and evaluating their performance. The best combination of parameters is then chosen for the final model.
To classify IRIS flowers, we first need to gather a dataset of IRIS flowers and their characteristics, such as the sepal length, sepal width, petal length, and petal width. These characteristics are then used as inputs for the Random Forest Classifier.
The Random Forest Classifier is trained using the dataset, and the best set of parameters is found using Grid Search Cross Validation. After the model is trained, it can be used to classify new IRIS flowers based on their characteristics.
In summary, the IRIS flower classification using SKLEARN Random Forest Classifier with Grid Search Cross Validation is a method to classify the IRIS flowers into its different types using ensemble learning method of multiple decision trees and fine-tuning the best parameter combination using Grid Search Cross Validation.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: IRIS Flower Classification using SKLEARN Random Forest Classifier with Grid Search Cross Validation.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
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