#### 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

## Logistic Regression

The logistic or logit model is one such non-linear model that has been effectively used for classification tasks in a number of different domains. In this section, we will use it to classify images of hand-written digits. In understanding the logistic model, we also take an important step in understanding the operation of a particularly powerful machine learning model, artificial neural networks. So, what exactly is the logistic model? Like the linear model, which is composed of a linear or straight-line function, the logistic model is composed of the standard logistic function, which, in mathematical terms, looks something like this:

Figure 4.8: Logistic function

In practical terms, when trained, this function returns the probability of the input information belonging to a particular class or group.

Say we would like to predict whether a single entry of data belongs to one of two groups. As in the previous example, in linear regression, this would equate to y being either...