This document is an introduction to scikit-learn, an open-source machine learning library for Python. It discusses predictive modeling and how scikit-learn fits into this domain. The presentation covers the basics of machine learning and predictive modeling, provides an overview of scikit-learn including its API and development process, and demonstrates examples of using common algorithms like logistic regression, support vector machines, and random forests.

1. Introduction to scikit-learn
Predictive modeling in Python
Olivier Grisel
Slides: ogrisel.github.io/decks/2019_intro_sklearn
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2. Agenda
Machine Learning refresher
Where do predictive models fit?
Scikit-learn
The open-source development process
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3. Predictive Modeling 101
Make predictions of outcome of repeated
events
Extract the structure of historical records
Statistical tools to summarize the training data
into an executable model
Alternative to hard-coded rules written by
experts
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21. Where do predictive models
fit?
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25. Scikit-learn
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26. Library of Machine Learning algorithms
Open Source project
Python / NumPy / SciPy / Cython
Simple fit / predict / transform API
Model Assessment, Selection, Ensembles
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27. Core team: Australia, China, France, Germany,
USA
Users: Scientific/Academic and
Business/Industry
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33. Linear Classifier
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(C=1, penalty='l1')
model.fit(X_train, y_train)
y_predicted = model.predict(X_test)
from sklearn.metrics import f1_score
f1_score(y_test, y_predicted)
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34. Support Vector Machine
from sklearn.svm import SVC
model = SVC(kernel="rbf", C=1.0, gamma=1e-4)
model.fit(X_train, y_train)
y_predicted = model.predict(X_test)
from sklearn.metrics import f1_score
f1_score(y_test, y_predicted)
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35. Random Forest
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=200)
model.fit(X_train, y_train)
y_predicted = model.predict(X_test)
from sklearn.metrics import f1_score
f1_score(y_test, y_predicted)
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38. Scikit-learn development
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50. Conclusion
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52. Secrets of the success of
Python (& R) in Data Science
Iterative exploration with built-in plotting tools
Low latency of single host in-memory
computing
Easy to install, easy to teach: no-sysadmin
required
Rich ecosystem of libraries
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53. Thank you for your attention!
https://scikit-learn.org
Slides: ogrisel.github.io/decks/2019_intro_sklearn
@ogrisel on twitter
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54. Background image credits
https://www.flickr.com/photos/jemimus/8533890844/
https://www.flickr.com/photos/antcaz/2249694239/
https://www.flickr.com/photos/benjamine-
s/14004414605
https://www.flickr.com/photos/a-herzog/9026372290
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