Hyperparameter Tuning with Grid Search and Random Search in Python

Описание: 📚 Python for AI and Machine Learning: From Beginner to Pro

🔍 In this lecture, we explore hyperparameter tuning to improve machine learning model performance — especially for real-world applications like crop health prediction.

Using the crop_health.csv dataset, we’ll walk you through:
✅ Cleaning and preparing your dataset
✅ Building a Random Forest Classifier
✅ Using GridSearchCV to exhaustively try all parameter combinations
✅ Using RandomizedSearchCV for faster tuning with large parameter spaces
✅ Evaluating accuracy, precision, and recall on test data
✅ Analyzing cross-validation scores for model stability and overfitting detection

🛠️ What You'll Learn:
📌 Why hyperparameters matter and how tuning improves your model
📌 Setting up GridSearchCV and RandomizedSearchCV in scikit-learn
📌 Understanding cross-validation metrics and how to interpret results
📌 Overfitting risks and how to address them (e.g., max_depth=None vs max_depth=5)
📌 Practical model evaluation and parameter tweaking

📈 Results Achieved in This Lecture:

Grid Search CV Accuracy: 0.98

Random Search CV Accuracy: 0.98

Test Accuracy: 0.97, Precision: 0.97, Recall: 0.93

Cross-Validation Scores: [0.981, 0.987, 0.969, 0.994, 0.975]

Mean CV Accuracy: 0.98, Standard Deviation: 0.01 (Great stability!)

💬 Action Steps:
👉 Try tuning with max_depth=5 to reduce overfitting
👉 Complete the coding exercise and share your CV scores in the comments
👉 Take the quiz to test your tuning knowledge

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