Create decision tree classifier to feed data and predict it Description:
In this project, I developed a machine learning model to classify iris flower species into three categories: Iris-setosa, Iris-versicolor, and Iris-virginica using the famous Iris dataset. The project involved data preprocessing, model training, evaluation, and visualization of decision trees.
Results:
Gini Criterion Decision Tree:
Test Accuracy: 96.67% Classification Report: Iris-setosa: Precision, Recall, F1-Score = 1.00 Iris-versicolor: Precision = 1.00, Recall = 0.92, F1-Score = 0.96 Iris-virginica: Precision = 0.91, Recall = 1.00, F1-Score = 0.95 Entropy Criterion Decision Tree:
Test Accuracy: 96.67% Classification Report: Iris-setosa: Precision, Recall, F1-Score = 1.00 Iris-versicolor: Precision = 1.00, Recall = 0.92, F1-Score = 0.96 Iris-virginica: Precision = 0.91, Recall = 1.00, F1-Score = 0.95