Random Error Quantification in Machine Learning
Last Updated on July 24, 2023 by Editorial Team
Author(s): Benjamin Obi Tayo Ph.D.
Originally published on Towards AI.
Every machine learning algorithm has an inherent random error that must be assessed and quantified
Image by Benjamin O. Tayo
Every machine learning model has an inherent random error. This error arises from the random nature in which the dataset is partitioned into training and testing sets. Using sklearn, the features matrix (X) and the target variable (y) can be partitioned as follows:
from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=i)
Note that by changing the random_state parameter (setting i to different integer values), we can randomly select different training and testing sets. Such a random process will introduce random fluctuations in the accuracy of the model. In this article, we will… Read the full blog for free on Medium.
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