Book Image

scikit-learn Cookbook - Second Edition

By : Trent Hauck
Book Image

scikit-learn Cookbook - Second Edition

By: Trent Hauck

Overview of this book

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively. The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.
Table of Contents (13 chapters)

Plotting an ROC curve without context

How to do it...

An ROC curve is a diagnostic tool for any classifier without any context. No context means that we do not know yet which error type (FP or FN) is less desirable yet. Let us plot it right away using a vector of probabilities, y_pred_proba[:,1]:

from sklearn.metrics import roc_curve

fpr, tpr, ths = roc_curve(y_test, y_pred_proba[:,1])
plt.plot(fpr,tpr)

The ROC is a plot of the FPR (false alarms) in the x axis and TPR (finding everyone with the condition who really has it) in the y axis. Without context, it is a tool to measure classifier performance.

Perfect classifier

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