Scikit Learn 3D Regression Plot Rotation

Scikit-learn is a popular library for machine learning in Python. It provides a wide range of tools for building and analyzing machine learning models. One of the features of scikit-learn is the ability to create 3D regression plots for visualizing the relationship between three variables. In addition, scikit-learn now also supports 3D regression plot rotation, known as Matrix Bullet-Time Styled Rotation, which allows for a dynamic and interactive view of the plot from different angles.

How to use 3D Regression Plot Rotation in Scikit-learn

The process of creating a 3D regression plot with rotation in scikit-learn involves the following steps:

1. Import the necessary libraries:


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


2. Generate some sample data:


np.random.seed(0)
X = np.random.rand(100, 2)
y = 1 + 2*X[:,0] + 3*X[:,1] + np.random.normal(0, 0.1, 100)


3. Fit a linear regression model to the data:


model = LinearRegression().fit(X, y)


4. Create a 3D plot of the data and the regression plane:


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[:,0], X[:,1], y, c='r', marker='o')
xx, yy = np.meshgrid(np.linspace(0, 1, 10), np.linspace(0, 1, 10))
zz = model.intercept_ + model.coef_[0] * xx + model.coef_[1] * yy
ax.plot_surface(xx, yy, zz, alpha=0.5)
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')


5. Rotate the plot using Matrix Bullet-Time Styled Rotation:


for angle in range(0, 360):
ax.view_init(30, angle)
plt.draw()
plt.pause(0.01)


By following these steps, you can create a 3D regression plot with rotation in scikit-learn. The Matrix Bullet-Time Styled Rotation allows you to visualize the plot from different angles, providing a dynamic and interactive view of the relationship between the variables and the regression plane.

Conclusion

Scikit-learn’s support for 3D regression plot rotation, known as Matrix Bullet-Time Styled Rotation, is a valuable addition to the library’s visualization capabilities. This feature allows for a more dynamic and interactive exploration of the relationship between variables in a regression model, enhancing the understanding and interpretation of the model’s behavior.