Decision-Tree-and-Logistic-Regression

Classification of the Iris Dataset using Decision Tree and Logistic Regression

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1. Aim To classify the Iris dataset using Decision Tree and Logistic Regression models, compare their performance using 5-fold cross-validation, and evaluate metrics such as accuracy, precision, recall, and F1-score.

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2. Objectives

(i) Train and evaluate two classification models:

Decision Tree Classifier Logistic Regression Classifier

(ii) Compare the generalization performance of both models using 5-fold cross-validation.

(iii) Analyze the performance using:

Confusion Matrix

Classification Report (Precision, Recall, F1-Score)

(iv) Identify strengths and limitations of both models, such as sensitivity to overfitting and consistency across folds.

Dataset

Name: Iris Dataset Features: 4 (Sepal Length, Sepal Width, Petal Length, Petal Width) Target Classes:

Setosa

Versicolor

Virginica

Source: Scikit-learn's load_iris dataset

Tools and Libraries

Programming Language: Python

Libraries Used:

Scikit-learn: For model implementation, evaluation, and cross-validation.

NumPy: For numerical operations.

Models:

Decision Tree Classifier

Logistic Regression