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Beginning Data Science with Python and Jupyter

You're reading from   Beginning Data Science with Python and Jupyter Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data

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Product type Paperback
Published in Jun 2018
Publisher
ISBN-13 9781789532029
Length 194 pages
Edition 1st Edition
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Author (1):
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Alex Galea Alex Galea
Author Profile Icon Alex Galea
Alex Galea
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Training Classification Models


As we've already seen in the previous lesson, using libraries such as scikit-learn and platforms such as Jupyter, predictive models can be trained in just a few lines of code. This is possible by abstracting away the difficult computations involved with optimizing model parameters. In other words, we deal with a black box where the internal operations are hidden instead. With this simplicity also comes the danger of misusing algorithms, for example, by overfitting during training or failing to properly test on unseen data. We'll show how to avoid these pitfalls while training classification models and produce trustworthy results with the use of k-fold cross validation and validation curves.

Subtopic A: Introduction to Classification Algorithms

Recall the two types of supervised machine learning: regression and classification. In regression, we predict a continuous target variable. For example, recall the linear and polynomial models from the first lesson. In...

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