Comparison with Other Text Classification Techniques

Implementation: Text Classification using Decision Trees

We will compare decision trees with other popular text classification algorithms such as Random Forest and Support Vector Machines.

Text Classification using Random Forest

Python3

from sklearn.ensemble import RandomForestClassifier

# Initialize and train a Random Forest classifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# Make predictions
y_pred = clf.predict(X_test)

# Evaluate the model
print("\nClassification Report:\n", classification_report(y_test, y_pred, target_names=newsgroups_test.target_names))

Output:

Classification Report:
                         precision    recall  f1-score   support

           alt.atheism       0.70      0.48      0.57       319
         comp.graphics       0.77      0.93      0.84       389
               sci.med       0.80      0.75      0.77       396
soc.religion.christian       0.74      0.82      0.78       398

              accuracy                           0.76      1502
             macro avg       0.75      0.74      0.74      1502
          weighted avg       0.75      0.76      0.75      1502

Text Classification using SVM

Python3

from sklearn.svm import SVC

# Initialize and train an SVM classifier
clf = SVC(kernel='linear', random_state=42)
clf.fit(X_train, y_train)

# Make predictions
y_pred = clf.predict(X_test)

# Evaluate the model
print("\nClassification Report:\n", classification_report(y_test, y_pred, target_names=newsgroups_test.target_names))

Output:

Classification Report:
                         precision    recall  f1-score   support

           alt.atheism       0.75      0.63      0.68       319
         comp.graphics       0.91      0.90      0.90       389
               sci.med       0.80      0.90      0.85       396
soc.religion.christian       0.80      0.82      0.81       398

              accuracy                           0.82      1502
             macro avg       0.82      0.81      0.81      1502
          weighted avg       0.82      0.82      0.82      1502

Observations

SVM outperforms both Random Forest and Decision Tree classifiers in terms of accuracy and overall performance, as indicated by the higher F1-score.
Random Forest performs relatively well but slightly lags behind SVM.
Decision Tree shows the lowest performance among the three classifiers, indicating the importance of choosing an appropriate algorithm for text classification tasks.

Text Classification using Decision Trees in Python

Text classification is the process of classifying the text documents into predefined categories. In this article, we are going to explore how we can leverage decision trees to classify the textual data.

Tags:

#AI-ML-DS With Python #AI-ML-DS #Machine Learning #Machine Learning