Important concepts related to machine learning

The linear classifier we presented in the previous section could look too simple. What if we use a higher degree polynomial? What if we also take as features not only the sepal length and width, but also the petal length and the petal width? This is perfectly possible, and depending on the sample distribution, it could lead to a better fit to the training data, resulting in higher accuracy. The problem with this approach is that now we must estimate not only the three original parameters (the coefficients for x1, x2, and the interception point), but also the parameters for the new features x3 and x4 (petal length and width) and also the product combinations of the four features.

Intuitively, we would ...

Get scikit-learn : Machine Learning Simplified now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.