Random forest algorithm example in Python

Random Forest (Random Forest) is an ensemble learning (Ensemble Learning) algorithm that improves accuracy and robustness by combining the prediction results of multiple decision trees. Random forests are widely used in various fields, such as finance, medical care, e-commerce, etc.

This article will introduce how to use Python to implement a random forest classifier and test it using the iris data set.

1. Iris Dataset

The Iris Dataset is a classic data set in machine learning. It contains 150 records, each record has 4 features and 1 category label. . The four characteristics are sepal length, sepal width, petal length and petal width, and the category label represents one of the three varieties of iris (Iris mountain, Iris versicolor, Iris virginia).

In Python, we can use scikit-learn, a powerful machine learning library, to load the iris data set. The specific operations are as follows:

from sklearn.datasets import load_iris

iris = load_iris()
X = iris.data
y = iris.target

2. Build a random forest classifier

It is very simple to build a random forest classifier using scikit-learn. First, we need to import the RandomForestClassifier class from sklearn.ensemble and instantiate an object:

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier(n_estimators=10)

Among them, the n_estimators parameter specifies the number of decision trees included in the random forest. Here, we set the number of decision trees in the random forest to 10.

Next, we need to divide the iris data set into training data and test data. Use the train_test_split function to randomly divide the data set into a training set and a test set:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Among them, the test_size parameter specifies the proportion of the test set, and the random_state parameter specifies the seed of the pseudo-random number generator to ensure that each time Running the program gives the same result.

We can then use the training data to train the random forest classifier:

rfc.fit(X_train, y_train)

3. Test the random forest classifier

Once the classifier has been trained, we can use Test data to test its performance. Use the predict function to make predictions on the test set and the accuracy_score function to calculate the accuracy of the model:

from sklearn.metrics import accuracy_score

y_pred = rfc.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

Finally, we can use the matplotlib library to visualize the decision boundary of the classifier to better understand the behavior of the classifier ：

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
z_min, z_max = X[:, 2].min() - .5, X[:, 2].max() + .5
xx, yy, zz = np.meshgrid(np.arange(x_min, x_max, 0.2), np.arange(y_min, y_max, 0.2), np.arange(z_min, z_max, 0.2))

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

Z = rfc.predict(np.c_[xx.ravel(), yy.ravel(), zz.ravel()])
Z = Z.reshape(xx.shape)
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y)
ax.set_xlabel('Sepal length')
ax.set_ylabel('Sepal width')
ax.set_zlabel('Petal length')
ax.set_title('Decision Boundary')

ax.view_init(elev=30, azim=120)
ax.plot_surface(xx, yy, zz, alpha=0.3, facecolors='blue')

plt.show()

The above code will obtain a three-dimensional image, in which the color of the data points represents the variety of iris flowers, and the decision boundary is represented by a translucent blue surface.

4. Summary

This article introduces how to use Python to implement a random forest classifier and use the iris data set for testing. Due to the robustness and accuracy of the random forest algorithm, it has broad application prospects in practical applications. If you are interested in this algorithm, it is recommended to practice more and read relevant literature.

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