Python Tutorial: Introducing Hyperparameters

Want to learn more? Take the full course at https://learn.datacamp.com/courses/ob... at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In the previous lesson, you learned what parameters are. You will now learn what exactly hyperparameters are, how to find and set them, as well as some tips and tricks for prioritizing your efforts. Let's get started. Hyperparameters are something that you set before the modeling process begins. You can think of them like the knobs and dials on an old radio. You tune the different dials and buttons and hope that a nice tune comes out. The algorithm does not learn the value of these during the modeling process. This is the crucial differentiator between hyperparameters and parameters. Whether you set it or the algorithm learns it and informs you. We can easily see the hyperparameters by creating an instance of the estimator and printing it out. Here we create the estimator with default settings and call the print function on our estimator. Those are all our different knobs and dials we can set for our model. There are a lot! But what do they all mean? For this we need to turn to the Scikit Learn documentation. Let us take the example of the 'n_estimators' hyperparameter. We can see in the documentation that it tells us the data type and the default value And it also provides a definition of what it means. We can set the hyperparameters when we create the estimator object. The default number of trees seems a little low, so let us set that to be 100. Whilst we are at it, let us also set the criterion to be 'entropy'. If we print out the model We can see the other default values remain the same, but those we set explicitly overrode the default values. What about our logistic regression model, what were the hyperparameters for that? We follow the same steps. Firstly we create a logistic regression estimator. Then we print it out We can see there are less