Ryan Curtin
c8eb3dceac
* Add pipeline to documentation homepage. * Fix for mobile devices. * Add little pipelines to go at the top of each page. * Overhaul index page. * Overhaul README to remove redundant material. * Add installation documentation. * Update pipelines. * Allow nesting of deeper details. * Add a pipeline to the top of the load/save page. * Add prerequisites link to main pipeline. * Add better but not finished sidebar. * Add a couple new documentation pages. * Fix URLs in svg. * Incremental checkin. * Fix Youtube URLs. * Incremental checkin. * Minor fixes. * Add first pass at evaluation/deployment pages. * Minor spacing and link fixes. * Flesh out a number of additional pages and write basic compilation documentation. * Fix some minor issues, and add Docker deployment page (not totally finished yet). * Add developer documentation landing page. * Hopefully getting close to the final set of changes here. * Remove this documentation for now. * Fix a few links, and the size of the sidebar. * Fix some additional links. * Fix a bunch more links. * Fix another link that now has a better place. * Refactor test-docs.sh to handle documentation that is a standalone program. * Fix file exclusions. * Fully qualify typename. * Update name of file. * Fix syntax error. * Remove files that are not meant to be compiled. * Also skip the quickstart. * Move quickstart entry to the top. * Remove gray coloring of binding documentation. * Update name of sidebar link. * Update to working link. * Fix Wikipedia anchor.
68 lines
2.4 KiB
Markdown
68 lines
2.4 KiB
Markdown
<object data="../img/pipeline-top-4.svg" type="image/svg+xml" id="pipeline-top">
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</object>
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# Modeling
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mlpack contains numerous different machine learning algorithms that can be used
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for modeling.
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*Note: this section is under construction and not all functionality is
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documented yet.*
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## Classification
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Classify points as discrete labels (`0`, `1`, `2`, ...).
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* [`AdaBoost`](methods/adaboost.md): Adaptive Boosting
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* [`DecisionTree`](methods/decision_tree.md): ID3-style decision tree
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classifier
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* [`HoeffdingTree`](methods/hoeffding_tree.md): streaming/incremental decision
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tree classifier
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* [`LinearSVM`](methods/linear_svm.md): simple linear support vector machine
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classifier
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* [`LogisticRegression`](methods/logistic_regression.md): L2-regularized
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logistic regression (two-class only)
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* [`NaiveBayesClassifier`](methods/naive_bayes_classifier.md): simple
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multi-class naive Bayes classifier
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* [`Perceptron`](methods/perceptron.md): simple Perceptron classifier
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* [`RandomForest`](methods/random_forest.md): parallelized random forest
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classifier
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* [`SoftmaxRegression`](methods/softmax_regression.md): L2-regularized
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softmax regression (i.e. multi-class logistic regression)
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## Regression
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Predict continuous values.
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* [`BayesianLinearRegression`](methods/bayesian_linear_regression.md):
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Bayesian L2-penalized linear regression
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* [`DecisionTreeRegressor`](methods/decision_tree_regressor.md): ID3-style
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decision tree regressor
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* [`LARS`](methods/lars.md): Least Angle Regression (LARS), L1-regularized and
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L2-regularized
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* [`LinearRegression`](methods/linear_regression.md): L2-regularized linear
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regression (ridge regression)
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## Clustering
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***NOTE:*** this documentation is still under construction and so some
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algorithms that mlpack implements are not yet listed here. For now, see
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[the mlpack/methods directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
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for a full list of algorithms.
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Group points into clusters.
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* [`MeanShift`](methods/mean_shift.md): clustering with the density-based mean
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shift algorithm
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## Geometric algorithms
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***NOTE:*** this documentation is still under construction and so no geometric
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algorithms in mlpack are documented yet. For now, see
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[the mlpack/methods directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
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for a full list of algorithms.
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Computations based on distance metrics.
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<!-- TODO: add some -->
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