How to Create a Random Forest in Python

In this guide, we’ll explore one of the fundamental Machine Learning algorithms: Random Forest. Random Forest is a powerful and versatile machine-learning method that involves training multiple decision trees and aggregating their results. It’s often used for regression and classification tasks, but also has methods for feature selections and anomaly detection.

Random Forest algorithm can be implemented easily using Python’s Scikit-Learn library which contains a highly-optimal, open-source implementation. We will walk you through the process of creating a Random Forest in Python using this library.

Step 1: Import Necessary Libraries

First, let’s import all the necessary libraries. We need Numpy and Pandas for data manipulation, and from Scikit-Learn we need the Random Forest Classifier and the train_test_split function.

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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

Step 2: Load and Prepare the Data

For this tutorial, we’ll use the well-known Iris dataset, which you can load directly from Scikit-Learn.

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from sklearn.datasets import load_iris
iris = load_iris()

Next, we convert the dataset to a Pandas DataFrame and split the data into training and testing sets:

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df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])
df['target'] = iris['target']
X_train, X_test, y_train, y_test = train_test_split(df[iris['feature_names']], df['target'], random_state=0)

Step 3: Create and Fit the Model

We’ll create a Random Forest Classifier with 100 trees and fit it to our training data. The more trees there are, the more robust the forest is. Reducing the number of trees will make the forest less prone to overfitting.

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clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)

Step 4: Predict and Evaluate the Model

Now, we can use the trained model to predict the class of iris for the test set. Then, by comparing the predicted values to the actuals, we can gauge the accuracy of our model.

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predicted = clf.predict(X_test)
accuracy = accuracy_score(y_test, predicted)

You might then want to check other performance metrics such as precision, recall, F-score, etc.

Step 5: Viewing Feature Importance

One great trait of random forest is that it’s very easy to measure the relative importance of each feature on the prediction. Sklearn provides a great tool for this that measures the importance of a feature by looking at how much the tree nodes use that feature, reducing impurity on average across all trees in the forest.

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importance = clf.feature_importances_

Full Code

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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
 
iris = load_iris()
df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])
df['target'] = iris['target']
 
X_train, X_test, y_train, y_test = train_test_split(df[iris['feature_names']], df['target'], random_state=0)
 
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
 
predicted = clf.predict(X_test)
accuracy = accuracy_score(y_test, predicted)  # Use accuracy_score
 
importance = clf.feature_importances_
 
print("Accuracy:", accuracy)
print("Feature Importance:", importance)
 
 
Accuracy: 0.9736842105263158
Feature Importance: [0.10739339 0.02757325 0.47156144 0.39347192]

Conclusion

By following this guide, you should now be familiar with creating a Random Forest in Python using the Scikit-Learn library. This is a great starting point from which you can begin to build even more complex and powerful machine-learning models.

Don’t hesitate to try it on your own datasets and tweak parameters to optimize it according to your needs.