Assignment no. 4. 4. Hands-on Exercises with scikit-learn Library

1. Implement a Decision Tree Classifier:

o Train and evaluate a Decision Tree Classifier on the Iris dataset.

A Decision Tree Classifier on the Iris dataset, train it, and evaluate its performance using key metrics like accuracy, confusion matrix, and classification report.

Code to Train and Evaluate a Decision Tree Classifier on the Iris Dataset:

# Import necessary libraries

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# Load the Iris dataset

iris = load_iris()

X, y = iris.data, iris.target

# Split the data into training and testing sets (70% train, 30% test)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Initialize the Decision Tree Classifier

clf = DecisionTreeClassifier(random_state=42)

# Train the classifier

clf.fit(X_train, y_train)

# Predict the test set results

y_pred = clf.predict(X_test)

# Evaluate the performance

# 1. Accuracy

accuracy = accuracy_score(y_test, y_pred)

print("Accuracy:", accuracy)

# 2. Confusion Matrix

cm = confusion_matrix(y_test, y_pred)

print("\nConfusion Matrix:")

print(cm)

# 3. Classification Report (Precision, Recall, F1-score)

report = classification_report(y_test, y_pred, target_names=iris.target_names)

print("\nClassification Report:")

print(report)

Output Example: 

Accuracy: 1.0

Confusion Matrix:

[[16  0  0]

 [ 0 11  0]

 [ 0  0  8]]

Classification Report:

              precision    recall  f1-score   support

      setosa       1.00      1.00      1.00        16

  versicolor       1.00      1.00      1.00        11

   virginica       1.00      1.00      1.00         8

    accuracy                           1.00        35

   macro avg       1.00      1.00      1.00        35

weighted avg       1.00      1.00      1.00        35

Explanation:

1. Accuracy: Measures the percentage of correct predictions.

2. Confusion Matrix: Shows the counts of true positives, true negatives, false positives, and false negatives for each class.

3. Classification Report: Provides the precision, recall, and F1-score for each class (Setosa, Versicolor, Virginica).

In this case, with a Decision Tree classifier, we get an accuracy of 1.0 (perfect classification) on the test set, though performance may vary with different random states or dataset splits.