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Scikit-learn: Machine learning in Python Brian Holt

Scikit-learn: Machine learning in Python Brian Holt. Project goals and vision. Machine learning for applications Ease of use Light and easy to install package A general-purpose high level language: Python High standards State-of-the-art algorithms

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Scikit-learn: Machine learning in Python Brian Holt

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  1. Scikit-learn: Machine learning in Python Brian Holt

  2. Project goals and vision • Machine learning for applications • Ease of use • Light and easy to install package • A general-purpose high level language: Python • High standards • State-of-the-art algorithms • High quality bindings: performance and fine control • Open Source • BSD license • Community driven

  3. API and design 1 • Design principles • Minimise number of object interfaces • Build abstractions for recurrant usecases • Simplicity, Simplicity, Simplicity • (no framework, no pipelines, no dataset objects) • Code sample >>> from sklearn import svm >>> classifier = svm.SVC() >>> classifier.fit(X_train, y_train) >>> y_pred = clf.predict(X_test)

  4. API and design 2 • All objects • estimator.fit(X_train, y_train) • Classification, regression, clustering • y_pred = estimator.predict(X_test) • Filters, dimension reduction, latent variables • X_new = estimator.transform(X_test) • Predictive models, density estimation • test_score = estimator.score(X_test) • One day: On-line learning • estimator.refit(X_train, y_train)

  5. Main features and algorithms • Supervised learning (classification and regression) • SVM - high quality libsvm bindings • Generalised linear models • Least squares, ridge regression, Orthogonal matching pursuit... • Nearest Neighbours • KDTree, BallTree • Gaussian Processes • Decision tree (CART) • Coming soon: Ensembles (boosting, bagging)

  6. Main features and algorithms cont. • Unsupervised learning • Gaussian mixture models • Manifold learning • Clustering • Model selection • Cross-Validation • Grid Search

  7. Demos • SVM classifier on iris dataset • Cross validation • PCA • Kmeans • Compare ML algorithms

  8. Conclusion • Scikit-learn • Machine learning without learning the machinery • Simplicity and readability Optimise only the critical parts Code review Unit testing Coding standards • Well-suited for applications • Used for large datasets • Building blocks for application-specific algorithms

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