Advanced Machine Learning with Python | Hyperparameter Tuning, Cross-Validation, and Decision Trees

Welcome back! In this video, we dive deeper into the world of Machine Learning. Building on our previous tutorial, we'll explore hyperparameter tuning, cross-validation, and introduce the powerful Decision Tree algorithm using Python and scikit-learn.

🔍 What You'll Learn:

Understanding and optimizing hyperparameters
Implementing GridSearchCV for hyperparameter tuning
The importance of cross-validation and how to perform it
Building and evaluating a Decision Tree classifier
📚 Resources:

scikit-learn Documentation
[Previous Video: Introduction to Machine Learning with Python](link to previous video)
💻 Code Snippets:

python
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# Hyperparameter Tuning with GridSearchCV
param_grid = {'n_neighbors': range(1, 31)}
grid_search = GridSearchCV(KNeighborsClassifier(), param_grid, cv=5)
print(f'Best parameters: {best_params}')

# Cross-Validation
cv_scores = cross_val_score(KNeighborsClassifier(n_neighbors=best_params['n_neighbors']), X, y, cv=10)
print(f'Cross-validation
CONTINUE
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python
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.std():.2f}')

# Building and Evaluating a Decision Tree Classifier
dt_classifier = DecisionTreeClassifier(random_state=42)
accuracy_dt = accuracy_score(y_test, y_pred_dt)
print(f'Decision Tree Accuracy: {accuracy_dt * 100:.2f}%')
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