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AI Activity Zone
Experiential Learning

Activity 1: AI Modelel
Activity 1: AI Mod
Create a hands-on lab activity to evaluate an AI model using a custom dataset. Specify the steps for dataset
creation, model training, and the application of model evaluation metrics such as Precision, Recall, and F1
Score. Explain the significance of using a dataset that includes diverse scenarios and real-world conditions
for robust model assessment.

Activity 2: Evaluation Metrics
Activity 2: Evaluation Metrics
Develop a lab experiment to optimize an AI model’s performance by tuning its parameters. Outline the steps
involved in selecting model parameters, adjusting them, and assessing the impact on evaluation metrics.
Discuss the trade-offs between precision and recall and explain the concept of finding the optimal balance
for effective model tuning.

Activity 3: Mod
Activity 3: Model Evaluationel Evaluation
Create a lab activity to implement cross-validation for robust model evaluation. Provide step-by-step
instructions for dataset splitting, model training, and the application of cross-validation techniques.
Explain how cross-validation helps mitigate issues such as overfitting and assess the model’s generalization
performance.

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