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Python Machine Learning

You're reading from   Python Machine Learning Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Length 454 pages
Edition 1st Edition
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Author (1):
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Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Toc

Table of Contents (15) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Machine Learning Algorithms for Classification FREE CHAPTER 3. A Tour of Machine Learning Classifiers Using Scikit-learn 4. Building Good Training Sets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Training Artificial Neural Networks for Image Recognition 13. Parallelizing Neural Network Training with Theano Index

Evaluating and tuning the ensemble classifier


In this section, we are going to compute the ROC curves from the test set to check if the MajorityVoteClassifier generalizes well to unseen data. We should remember that the test set is not to be used for model selection; its only purpose is to report an unbiased estimate of the generalization performance of a classifier system. The code is as follows:

>>> from sklearn.metrics import roc_curve
>>> from sklearn.metrics import auc
>>> colors = ['black', 'orange', 'blue', 'green']
>>> linestyles = [':', '--', '-.', '-']
>>> for clf, label, clr, ls \
...         in zip(all_clf, clf_labels, colors, linestyles):
...     # assuming the label of the positive class is 1
...     y_pred = clf.fit(X_train, 
...                      y_train).predict_proba(X_test)[:, 1]
...     fpr, tpr, thresholds = roc_curve(y_true=y_test, 
...                                      y_score=y_pred)
...     roc_auc = auc(x=fpr, y...
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