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Soubeer Koley 2024-05-27 19:23:51 +05:30
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@ -61,7 +61,7 @@ Hyperparameter tuning can significantly improve the performance of a Random Fore
#### Classification Example
Below is a simple example of using Random Forest for a classification task with the Iris dataset.
'''
```
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
@ -69,6 +69,7 @@ from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
# Load dataset
iris = load_iris()
X, y = iris.data, iris.target
@ -90,13 +91,13 @@ accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")
print("Classification Report:\n", classification_report(y_test, y_pred))
'''
```
#### Feature Importance
Random Forest provides a way to measure the importance of each feature in making predictions.
'''
```
import matplotlib.pyplot as plt
# Get feature importances
@ -115,11 +116,11 @@ plt.bar(range(X.shape[1]), importances[indices], align='center')
plt.xticks(range(X.shape[1]), indices)
plt.xlim([-1, X.shape[1]])
plt.show()
'''
```
#### Hyperparameter Tuning
Using Grid Search for hyperparameter tuning.
'''
```
from sklearn.model_selection import GridSearchCV
# Define the parameter grid
@ -138,11 +139,11 @@ grid_search.fit(X_train, y_train)
# Print the best parameters
print("Best parameters found: ", grid_search.best_params_)
Regression Example
```
#### Regression Example
Below is a simple example of using Random Forest for a regression task with the Boston housing dataset.
python
Copy code
```
import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
@ -171,10 +172,11 @@ mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"Mean Squared Error: {mse:.2f}")
print(f"R^2 Score: {r2:.2f}")
Conclusion
```
### Conclusion
Random Forest is a powerful and flexible machine learning algorithm that can handle both classification and regression tasks. Its ability to create an ensemble of decision trees leads to robust and accurate models. However, it is important to be mindful of the computational cost associated with training multiple trees.
References
### References
Scikit-learn Random Forest Documentation
Wikipedia: Random Forest
Machine Learning Mastery: Introduction to Random Forest