Python Tutorial : Linear Classifiers in Python

Want to learn more? Take the full course at https://learn.datacamp.com/courses/linear-classifiers-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to the course on logistic regression and support vector machines with Python! In this first chapter, we'll cover the syntax for using these classifiers in scikit-learn. In Chapter 2, we'll go into a more conceptual study of loss functions. This will form the basis for going deeper into logistic regression and support vector machines (or SVMs) in Chapters 3 and 4. In this course, we'll assume you've taken the prerequisite courses or have a similar level of knowledge. In this video, we'll briefly review the standard syntax of the popular machine learning package scikit-learn, which was covered in the prerequisite course on supervised learning. We'll continue to use scikit-learn extensively in this course. To remind you, supervised learning refers to learning a relationship from examples of input-output pairs, usually called X and y. There are a few typical steps of supervised learning. First, let's load the newsgroups data from scikit-learn's repository of built-in datasets. We can inspect the shape of X and y and see that we have about 11,000 training examples, each with about 130,000 features. In this case the features are derived from the words appearing in each news article, and the y-values are the article topics, which is what we're trying to predict. Next, we can import the k nearest neighbors classifier, or KNN for short. We instantiate the classifier, and store it in the variable knn. This is the step where we specify model hyperparameters, like the number of neighbors for KNN. Next, we can fit the model using the "fit" method. This is standard syntax across all of scikit-learn. Then, we can make predictions on any data set, including the original training set X. The variable y_pred now contains one entry per row of X with the