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How can scikit-learn package be used to transform an array of specific size to a different size?
Scikit−learn, commonly known as sklearn is a library in Python that is used for the purpose of implementing machine learning algorithms. It is an open-source library hence it can be used free of cost. It is powerful and robust, since it provides a wide variety of tools to perform statistical modelling. This includes classification, regression, clustering, dimensionality reduction, and much more with the help of a powerful, and stable interface in Python. The library is built on Numpy, SciPy and Matplotlib libraries.
It can be installed using the ‘pip’ command as shown below −
pip install scikit−learn
This library focuses on data modelling. An array of different size can be transformed to an array of an entirely different size, using scikit−learn package.
Following is an example −
from sklearn.preprocessing import PolynomialFeatures import numpy as np Y = np.arange(12) print("The original dimensions of the ndarray") print(Y.shape) print("The changed dimensions of the ndarray") x = Y.reshape(3, 4) print(x.shape) poly = PolynomialFeatures(degree=2) print(poly.fit_transform(x))
The original dimensions of the ndarray (12,) The changed dimensions of the ndarray (3, 4) [[ 1. 0. 1. 2. 3. 0. 0. 0. 0. 1. 2. 3. 4. 6. 9.] [ 1. 4. 5. 6. 7. 16. 20. 24. 28. 25. 30. 35. 36. 42. 49.] [ 1. 8. 9. 10. 11. 64. 72. 80. 88. 81. 90. 99. 100. 110. 121.]]
The required packages are imported, and they are given alias names for ease of use.
The values for data points ‘x’ and ‘y’ are generated using NumPy library.
The details of the data generated is displayed on the console.
The ‘PolynomialFeatures’ function is called.
This function call is assigned to a variable.
This variable is fit to the model.
The data fit to the model is displayed on the console.
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