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开源软件名称(OpenSource Name):ledell/useR-machine-learning-tutorial开源软件地址(OpenSource Url):https://github.com/ledell/useR-machine-learning-tutorial开源编程语言(OpenSource Language):Jupyter Notebook 99.9%开源软件介绍(OpenSource Introduction):useR! Machine Learning TutorialuseR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive OverviewThis tutorial contains training modules for six popular supervised machine learning methods:
Here are some practical, related topics we will cover for each algorithm:
Instructions for how to install the necessary software for this tutorial is available here. Data for the tutorial can be downloaded by running Dimensionality IssuesCertain algorithms don't scale well when there are millions of features. For example, decision trees require computing some sort of metric (to determine the splits) on all the feature values (or some fraction of the values as in Random Forest and Stochastic GBM). Therefore, computation time is linear in the number of features. Other algorithms, such as GLM, scale much better to high-dimensional (n << p) and wide data with appropriate regularization (e.g. Lasso, Elastic Net, Ridge). SparsityAlgorithms can deal with data sparsity (where many of the feature values are zero) in different ways. In some algorithms there are ways to speed up the computations if sparsity is present, so it's good to know if these shortcuts are available. NormalizationSome algorithms such as Deep Neural Nets and GLMs require that data be normalized for effective interpretation of the models. Tree-based algorithms (Decision Trees, Random Forest, Gradient Boosting Machines) do not require normalization. Tree-based methods only use information about whether a value is greater than or less than a certain value (e.g. x > 7 vs. x ≤ 7), the values themselves do not matter. Categorical DataAlgorithms handle categorical data differently. Some algorithms such as GLM and Deep Neural Nets require that a categorical variable be expanded into a set of indicator variables, prior to training. With tree-based methods and software that supports it, there are ways to get around this requirement, which allows the algorithm to handle the categorical features directly. It is important to be cognizant of the cardinality of your categorical features before training, as additional pre-processing (collapsing categories, etc) may be beneficial with high-cardinality features. Missing DataAssuming the features are missing completely at random, there are a number of ways of handling missing data:
For most learning methods, the imputation approach (3) is necessary. The simplest tactic is to impute the missing value with the mean or median of the nonmissing values for that feature. If the features have at least some moderate degree of dependence, one can do better by estimating a predictive model for each feature given the other features and then imputing each missing value by its prediction from the model. Some software packages handle missing data automatically, although many don't, so it's important to be aware if any pre-processing is required by the user. Class ImbalanceAlgorithms that optimize a metric such as accuracy may fail to perform well on training sets that contain a significant degree of class imbalance. Certain algorithms, such as GBM, allow the user to optimize a performance metric of choice, which is useful when you have a highly imbalanced training set. OverfittingIt is always good to pay attention to the potential of overfitting, but certain algorithms and certain implementations are more prone to this issue. For example, when using Deep Neural Nets and Gradient Boosting Machines, it's always a good idea to check for overfitting. SoftwareFor each algorithm, we will provide examples of open source R packages that implement the algorithm. All implementations are different, so we will provide information on how each of the implementations differ. ScalabilityWe will address scalability issues inherent to the algorithm and discuss algorithmic or technological solutions to scalability concerns for "big data." ResourcesWhere to learn more?
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