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AI Testing

AI Testing Process

Validating AI Models

Every intelligent system requires assessment to ensure reliability.

Verifying Accuracy

Compare predictions with actual values to measure performance.

Evaluate Model Performance

Generate Unseen Data

Introduce fresh inputs and observe whether the system predicts correctly.

Example: Testing with New Inputs

// Evaluate AI Model with Unknown Data 
const samples = 500;
 for (let i = 0; i < samples; i++) {  
    let x = Math.random() * xMax;  
    let y = Math.random() * yMax;   
    let prediction = model.compute([x, y, model.bias]);   
    let shade = "black";   
    if (prediction === 0) shade = "blue";   
   graph.plotPoint(x, y, shade); 
}

Identifying Mistakes

Count Incorrect Outputs

Track the number of misclassifications to gauge effectiveness.

Example: Error Tracking

Optimizing Model

Improve System Performance

Refine the algorithm for better predictions.

Enhancement Methods:

  • Modify adjustment rate.
  • Increase dataset volume.
  • Extend iteration count.

Testing ensures AI models function accurately, making them reliable for real-world applications.

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What You'll Learn:
  • 📌 AI/ML Model Evaluation and Validation in Machine Learning
  • 📌 Know How Gen AI is Changing the World of Software Testing | GenAI for Software Testers