A Beginner's Guide to Machine Learning with Scikit-Learn

A Beginner’s Guide to Machine Learning with Scikit-LearnSarah Guido

PyTennessee 2014

All about me

• Grad student at the University of Michigan• Data analyst for HathiTrust• Organizer of Ann Arbor PyLadies chapter

My talk

• Machine learning and scikit-learn• Supervised and unsupervised learning• Preprocessing, validation and testing, strategies for machine learning

What is machine learning?

• Application of algorithms that learn from examples

• Representation and generalization

Why should we care?

• Useful in every day life• Email spam, handwriting analysis, stock market

analysis, Netflix

• Especially useful in data analysis• Feature extraction, linear regression, classification,

clustering

Machine Learning Vocab

• Instance• Feature• Class• Categorical

• Nominal• Ordinal

• Continuous

Machine Learning VocabFeature Class

Instance

Scikit-Learn

• Machine learning module• Open-source• Built-in datasets• Good resources for learning

Scikit-Learn

• Model = EstimatorObject()• Model.fit(dataset.data, dataset.target)

• dataset.data = dataset• dataset.target = labels

• Model.predict(dataset.data)

Scikit-Learn

• Supervised• Unsupervised• Semi-supervised• Reinforcement learning• Neural networks• …and many more!

Supervised learning

• Labeled data• You know what you’re looking for• Classification: predict categorical labels• Regression: predict continuous target variables

Classification

• Categorical variables• Relationship between instance and feature• Classification algorithms == classifiers

Classification

• Naïve Bayes classifier• Features are independent• Fast performance• Decent classifier

Classification

• Car evaluation dataset-UCI• Features: buying price, the maintenance price, the number of doors, the number of seats, the size of the trunk, and the safety ranking

• Labels: unacceptable, acceptable, good, or very good

Classification

Classification

Classification

Unsupervised algorithms

• Unlabeled data• You might have no idea what you’re looking for• Clustering: splitting observations into groups• Dimensionality reduction: flatten data to fewer dimensions

Clustering

• Exploring the data• Similar objects in the same group• Distance between data points

Clustering

• K-means clustering• Three steps

• Chooses initial cluster centers• Assigns data instance to cluster• Recalculates cluster center

• Efficient

Clustering

Clustering

Clustering

Data preprocessing

• Encoding categorical features

Data preprocessing

Data preprocessing

Data preprocessing

• Split the dataset into training and test data

Validation and testing

• Model evaluation

• Cross-validation

Good strategies

• Avoid overfitting• Use lots of data• Intuition fails in high dimensions

My materials

• Scikit-learn.org documentation and tutorials• Machine learning class at U of M• Scikit-learn talks

Resources

• Scikit-learn documentation and tutorials• scikit-learn.org/stable/documentation.html

• Other resources• http://archive.ics.uci.edu/ml/datasets.html• Mldata.org

• Videos• Scikit-learn tutorial: http://vimeo.com/53062607• Intro to scikit-learn: http://vimeo.com/72859487

Contact me!

• @sarah_guido• Linkedin.com/sarahguido• github.com/sarguido