Logistic Regression Assignment

Problem Description

In this assignment, you will train a Logistic Regression model, a fundamental algorithm for binary and multi-class classification. You will use the famous Iris dataset to classify species of iris flowers.

Instructions

1. Open the assignment.py file.
2. You will find a function definition: train_logistic_regression_on_iris().
3. Your tasks are to:
   • Load the Iris dataset from sklearn.datasets.
   • Split the data into training and testing sets (80% train, 20% test).
   • Create and train a LogisticRegression model.
   • Make predictions on the test set.
   • Calculate and return the accuracy of the model.

Hints

• Use load_iris() to get the data.
• Use train_test_split from sklearn.model_selection. Set random_state=42 for reproducibility.
• The LogisticRegression model is in sklearn.linear_model.
• Use model.fit() to train, model.predict() to predict, and accuracy_score from sklearn.metrics to evaluate.

Further Exploration (Optional)

• The trained model has an attribute model.coef_. What does this attribute represent? How can it tell you about the importance of different features?
• What is regularization in logistic regression? Look at the penalty and C parameters of the LogisticRegression model.
• How would you get a confusion matrix for your model's predictions? (Hint: confusion_matrix from sklearn.metrics)