Hyperparameter Tuning Lab NEP 2020

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NEP 2020 Aligned: Experiential & Competency-Based Learning

This lab implements NEP 2020 principles: hands-on experimentation, critical thinking through parameter analysis, multidisciplinary application in agriculture, and outcome-based learning with real-time feedback.

Experiential Learning Critical Thinking Competency-Based Outcome-Based Education Multidisciplinary Interactive Visualization
Dataset & Model
Agricultural Dataset
Algorithm
Hyperparameters
Data Configuration
Train/Test Split 80/20
Higher training % = more data to learn, less to validate
Cross-Validation Folds 5
Noise Level 10%
Simulates real-world data imperfections

Training model...

Model Performance
Train Accuracy
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Test Accuracy
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CV Score
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Overfit Gap
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Training Time
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Model Status
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Visualizations

Train vs Test Accuracy

Bias-Variance Tradeoff

Feature Importance

Complexity vs Time

Learning Curve (Train vs Validation over Epochs)

Loss Curve

Validation Score per Fold

Run History Comparison

# Algorithm Key Params Train Test Gap Status

Hyperparameter Sensitivity Analysis

Shows how test accuracy changes as the primary hyperparameter varies (other params held constant).

Learning Objectives (NEP 2020 Outcomes)
CO1: Distinguish between model parameters and hyperparameters
CO2: Analyze the impact of hyperparameters on model performance
CO3: Identify overfitting and underfitting through visual cues
CO4: Apply systematic tuning strategies to optimize agricultural ML models