[Translation] scikit-learn 0.18 tutorial Statistical learning tutorial for scientific data processing

Google translated http://scikit-learn.org/0.18/tutorial/statistical_inference/index.html. scikit-learn 0.18 Tutorial Table of Contents Previous tutorial

Statistical learning

Machine learning (https://en.wikipedia.org/wiki/Machine_learning) is an increasingly important technology due to the rapidly growing size of experimental science datasets. We are tackling a variety of problems, from building predictive functions that link different observations to classifying observations or learning the structure of unlabeled datasets. This tutorial will show you how to use machine learning techniques for the purpose of Statistical Inference (https://en.wikipedia.org/wiki/Statistical_inference) as a result of statistical learning with data as clues. scikit-learn is a classic machine learning algorithm for scientific Python packages (NumPy, SciPy, [matplotlib]. ](Http://matplotlib.org/)) is a tightly organized Python module integrated into the world.

Statistical learning: Settings and estimator objects in scikit-learn

Supervised learning: Predicting output variables from high-dimensional observations

--Curse of dimensionality and nearest neighbor --Linear model: from regression to sparseness --Support Vector Machine (SVM)

Model selection: Selection of estimator and its parameters

--Score, cross-validated score --Cross-validation generator --Grid search and cross-validated estimator

Unsupervised learning: seeking data representation

--Clustering: Group observations together --Decomposition: From signal to component and loading

Put everything together

--Pipeline processing --Face recognition by eigenface --Open problem: Stock market structure

Search for help

--Project mailing list --Q & A community with machine learning practitioners

Next tutorial

2010 --2016, scikit-learn developers (BSD license).

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