Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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Evaluating regression performance

So far, we've covered three popular regression algorithms in depth and implemented them from scratch by using several prominent libraries. Instead of judging how well a model works on testing sets by printing out the prediction, we need to evaluate its performance with the following metrics, which give us better insights:

  • The MSE, as I mentioned, measures the squared loss corresponding to the expected value. Sometimes the square root is taken on top of the MSE in order to convert the value back into the original scale of the target variable being estimated. This yields the root mean squared error (RMSE). Also, the RMSE has the benefit of penalizing large errors more since we first calculate the square of an error.
  • The mean absolute error (MAE) on the other hand measures the absolute loss. It uses the same scale as the target variable and gives us an idea of how close the predictions are to the actual values...