Learning AI with Kaggle | Intermediate Machine Learning | Exercise: Cross-Validation

🌟 Optimize Your Models: Master Cross-Validation for Hyperparameter Tuning! 🌟

Ready to fine-tune your machine learning models for peak performance? In this Kaggle Intermediate Machine Learning exercise on Cross-Validation, we're not just testing models – we're systematically selecting the best parameters for them!

The Foundation: Our Baseline Pipeline
We'll start with a familiar setup: a Pipeline that efficiently preprocesses our numeric housing data (imputing missing values with SimpleImputer) and trains a RandomForestRegressor. We'll remind ourselves of the previous lesson's cross_val_score function, which provides an average Mean Absolute Error (MAE) across multiple folds.

Step 1: Building a Smart Scoring Function
The core of this exercise is creating a flexible function, get_score, that:

Takes n_estimators (the number of trees in our Random Forest) as an input parameter.
Constructs a Pipeline with a SimpleImputer and RandomForestRegressor (with random_state=0).
Utilizes cross_val_score with three cross-validation folds (instead of five, for quicker iteration!).
Returns the average MAE for a given n_estimators value.
This function will be our powerhouse for systematic parameter testing!

Step 2: Testing Different Parameter Values
With get_score in hand, we'll embark on a crucial experiment:

We'll test eight different values for n_estimators, ranging from 50 to 400 (in steps of 50).
The average MAE for each n_estimators value will be stored in a Python dictionary.
This systematic exploration is the essence of hyperparameter tuning with cross-validation!

Visualizing Our Results & Finding the Best Parameter:
To easily identify the optimal n_estimators, we'll:

Clearly identify the n_estimators value that yields the lowest (best) average MAE. This visual approach makes model optimization intuitive and effective.
Key Takeaways:
Leverage cross-validation as a powerful tool for hyperparameter optimization.
Develop a reusable function to efficiently test different model parameters.
Systematically evaluate model performance across a range of parameter values.
Visualize results to identify the optimal parameter settings for your model.
🚀 What's Next: Diving into Gradient Boosting with XGBoost!

This exercise is just the beginning of hyperparameter tuning! While we touched upon it, we'll continue our journey to more advanced and powerful machine learning techniques. Next up, we're exploring Gradient Boosting – a state-of-the-art technique that consistently delivers high-performance results across diverse datasets!

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