WEKA Classifier Comparison

WEKA Classifier Comparison

In this homework, we explore the WEKA machine learning tool and how WEKA can be very helpful in the small to medium size research project.

To start, please

1. Download and install the WEKA machine learning tool on your machine.

2. Download two datasets (bank, credit-Dataset).

3. Explore the WEKA machine learning tool using the aforementioned datasets and compare at least five different classifiers based on their performance metrics.

4. Submit your homework on D2L.

Deliverable: WEKA Classifier Comparison

· Your report should include screenshots of your implementation using WEKA. Ensure that you capture the entire WEKA environment, not just the results. Please compile the screenshots in a Word document, provide a brief one-sentence explanation for each, and then submit the document.

• Download and install the WEKA machine learning tool on your machine,

• Download two datasets (bank, credit-Dataset),

• Explore the WEKA machine learning tool using the aforementioned datasets and compare at least five different classifiers based on their performance metrics,

• Submit your homework on D2L,

• Your report should include screenshots of your implementation using WEKA,

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Comprehensive Answer (General Guidance)

Step 1: Download and Install WEKA

• Go to the official WEKA site: https://www.cs.waikato.ac.nz/ml/weka/.

• Download the latest stable version for your operating system.

• Install the tool following the on-screen prompts. Once installed, open WEKA and you should see the GUI Chooser window with options like Explorer, Experimenter, KnowledgeFlow, and SimpleCLI.

Step 2: Download Datasets

• The bank dataset can be obtained from the UCI Machine Learning Repository (Bank Marketing Dataset).

• The credit dataset (such as the German Credit Dataset or Credit Approval Dataset) can also be downloaded from UCI or Kaggle.

• Save the files in CSV or ARFF format. If CSV, use WEKA’s CSV Loader to convert them into ARFF for easier processing.

Step 3: Explore WEKA with Datasets

Open WEKA Explorer: WEKA Classifier Comparison

1.  Preprocess Tab

   • Load the dataset (bank or credit).

   • Observe the attribute list, number of instances, and data distribution.

   • Apply filters if necessary (e.g., Normalize, Discretize).

2. Classify Tab

   • Choose classification algorithms (classifiers).

   • Select the test option: Use training set, Supplied test set, Cross-validation (10-fold recommended).

   • Run the classifiers and review the results (accuracy, confusion matrix, precision, recall, F-measure).

Step 4: Compare Five Classifiers

For both datasets, you can test classifiers such as:

1. J48 (Decision Tree) – Simple tree-based classifier, interpretable.

2. Naïve Bayes – Probabilistic model, fast and effective on small data.

3. Logistic Regression – Well-suited for binary outcomes.

4. k-Nearest Neighbors (IBk) – Instance-based learner, sensitive to distance metrics.

5. Random Forest – Ensemble method, usually high accuracy and robust.

Performance Metrics to Compare:

• Accuracy: % of correctly classified instances.

• Precision: Correctly predicted positives out of all predicted positives.

• Recall (Sensitivity): Correctly predicted positives out of all actual positives.

• F1-score: Balance between precision and recall.

• ROC Area (AUC): Performance across thresholds.

Step 5: Document Your Work

• Take screenshots of each step in WEKA:

  • Loading dataset,

  • Running classifiers,

  • Results (confusion matrix, summary output).

• Paste screenshots into a Word document.

• Add one-sentence explanations under each screenshot (e.g., “This screenshot shows the results of running J48 on the credit dataset, achieving 82% accuracy with a balanced precision-recall tradeoff.”).

• Write a short comparison table summarizing classifier performance across both datasets.

General Findings (what you might expect):

• Naïve Bayes: Performs well on smaller, clean datasets; sometimes struggles with correlated features.

• J48: Easy to interpret but can overfit.

• Logistic Regression: Stable and interpretable; good with linear relationships. WEKA Classifier Comparison

• k-NN: Effective but computationally expensive for large datasets.

• Random Forest: Usually best performance overall with strong generalization.

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