#### Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
Applied Supervised Learning with Python
Preface
Free Chapter
Python Machine Learning Toolkit
Exploratory Data Analysis and Visualization
Regression Analysis
Classification
Ensemble Modeling
Model Evaluation

## Evaluation Metrics

Evaluating a machine learning model is an essential part of any project: once we have allowed our model to learn from the training data, the next step is to measure the performance of the model. We need to find a metric that can not only tell us how accurate the predictions made by the model are, but also allow us to compare the performance of a number of models so that we can select the one best suited for our use case.

Defining a metric is usually one of the first things we should do when defining our problem statement and before we begin the EDA, since it's a good idea to plan ahead and think about how we intend to evaluate the performance of any model we build and how to judge whether it is performing optimally or not. Eventually, calculating the performance evaluation metric will fit into the machine learning pipeline.

Needless to say, evaluation metrics will be different for regression tasks and classification tasks, since the output values in the former are continuous...