#### 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

## Bagging

The term bagging is derived from a technique called bootstrap aggregation. In order to implement a successful predictive model, it's important to know in what situation we could benefit from using bootstrapping methods to build ensemble models. In this section, we'll talk about a way to use bootstrap methods to create an ensemble model that minimizes variance and look at how we can build an ensemble of decision trees, that is, the Random Forest algorithm. But what is bootstrapping and how does it help us build robust ensemble models?

### Bootstrapping

The bootstrap method refers to random sampling with replacement, that is, drawing multiple samples (each known as a resample) from the dataset consisting of randomly chosen data points, where there can be an overlap in the data points contained in each resample and each data point has an equal probability of being selected from the overall dataset:

Figure 5.5: Randomly choosing data points

From the previous diagram, we can see that each of...