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How to generate random regression problems using Python Scikit-learn?
Python Scikit-learn provides us make_regression() function with the help of which we can generate a random regression problem. In this tutorial, we will learn to generate random regression problems and random regression problems with sparse uncorrelated design.
Random Regression Problem
To generate a random regression problem using Python Scikit-learn, we can follow the below given steps −
Step 1 − Import the libraries sklearn.datasets.make_regression and matplotlib which are necessary to execute the program.
Step 2 − Provide the number of samples and other parameters.
Step 3 − Use matplotlib library to set the size and style of the output figure.
Step 4 − Plot the regression problem using matplotlib.
Example
In the below example, we will be generating regression problem with 500 samples.
# Importing libraries from sklearn.datasets import make_regression from matplotlib import pyplot as plt from matplotlib import style import seaborn as sns # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Creating and plotting the regression problem style.use("Solarize_Light2") r_data, r_values = make_regression(n_samples=500, n_features=1, n_informative=2, noise=1) plt.scatter(r_data[:,0],r_values,cmap='rocket'); plt.show()
Output
It will produce the following output −
Random Regression Problem with Sparse Uncorrelated Design
Python Scikit-learn provides us make_sparse_uncorrelated() function with the help of which we can generate a random regression problem with uncorrelated design.
To do so, we can take the below given steps −
Step 1 − Import the libraries sklearn.datasets.make_sparse_uncorrelated and matplotlib which are necessary to execute the program.
Step 2 − Provide the number of samples and other parameters.
Step 3 − Use matplotlib library to set the size and style of the output figure.
Step 4 − Plot the regression problem using matplotlib.
Example
In the below example, we will be generating regression problem with 500 samples and 4 features. The by default value of n_features parameter is 10.
# Importing libraries from sklearn.datasets import make_sparse_uncorrelated from matplotlib import pyplot as plt from matplotlib import style # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Creating the regression problem with sparse uncorrelated design X, y = make_sparse_uncorrelated(n_samples=500, n_features=4) # Plotting the dataset style.use("Solarize_Light2") plt.figure(figsize=(7.50, 3.50)) plt.title("Random regression problem with sparse uncorrelated design", fontsize="12") plt.scatter(X[:,0],y,edgecolor="k"); plt.show()
Output
It will produce the following output −
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