statistics and machine learning toolbox 快速入门 -凯发k8网页登录

statistics and machine learning toolbox™ 提供了用于描述数据、分析数据以及为数据建模的函数和 app。您可以使用描述性统计量、可视化和聚类进行探索性数据分析，对数据进行概率分布拟合，生成进行蒙特卡罗模拟的随机数，以及执行假设检验。回归和分类算法允许您使用分类和回归学习器以交互方式，或使用 automl 以编程方式从数据做出推断并构建预测模型。

对于多维数据分析和特征提取，工具箱提供主成分分析 (pca)、正则化、降维和特征选择方法，使您能够识别具有最佳预测能力的变量。

工具箱提供有监督、半监督和无监督的机器学习算法，包括支持向量机 (svm)、提升决策树、k-均值和其他聚类方法。您可以应用部分依赖图和 lime 等可解释性方法，并自动生成 c/c 代码用于嵌入式部署。许多工具箱算法可用于太大而无法放入内存的数据集。

教程

• machine learning in matlab
  

  discover machine learning capabilities in matlab^® for classification, regression, clustering, and deep learning, including apps for automated model training and code generation.

• train classification models in classification learner app
  

  workflow for training, comparing and improving classification models, including automated, manual, and parallel training.

• train regression models in regression learner app
  

  workflow for training, comparing and improving regression models, including automated, manual, and parallel training.

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  visually compare the empirical distribution of sample data with a specified distribution.

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  generate random samples from specified probability distributions, and displays display the samples as histograms.

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  understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.

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  address statistical modeling problems with active data collection.