IRIS Flower Classification using SKLEARN DecisionTree Classifier with Monte Carlo Cross Validation

IRIS Flower Classification using SKLEARN DecisionTree Classifier with Monte Carlo 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 Decision Tree Classifier and a method called Monte Carlo Cross Validation.

A Decision Tree Classifier is a type of algorithm that is used to classify items into different categories. It works by creating a tree-like structure, where each branch represents a decision that needs to be made. The leaves of the tree represent the final classification of the item.

Monte Carlo Cross Validation is a method used to evaluate the performance of the Decision Tree Classifier. It works by repeatedly selecting different subsets of the data and training the model on these subsets. The performance of the model is then evaluated on a separate set of data. The process is repeated multiple times to get an average performance of the 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 Decision Tree Classifier.

The Decision Tree Classifier is trained using the dataset, and its performance is evaluated using Monte Carlo 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 DecisionTree Classifier with Monte Carlo Cross Validation is a method to classify the IRIS flowers into its different types using a tree-like structure of decision making algorithm and evaluating the model’s performance using Monte Carlo Cross Validation. It trains the model multiple times using different subsets of data to get an average performance of the model.

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 DecisionTree Classifier with Monte Carlo Cross Validation.


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