本页对应的英文页面已更新,但尚未翻译。 若要查看最新内容,请点击此处访问英文页面。

判别分析

正则化线性判别分析和二次判别分析

要以交互方式训练判别分析模型,可以使用 Classification Learner App。为了获得更大的灵活性,可以在命令行界面中使用 fitcdiscr 来训练判别分析模型。训练模型后,可将模型和预测变量数据传递给 predict,以预测标签或估计后验概率。

App

Classification Learner使用有监督的机器学习训练模型以对数据进行分类

函数

全部展开

fitcdiscrFit discriminant analysis classifier
makecdiscrConstruct discriminant analysis classifier from parameters
compactCompact discriminant analysis classifier
cvshrinkCross-validate regularization of linear discriminant
crossvalCross-validated discriminant analysis classifier
kfoldEdgeClassification edge for observations not used for training
kfoldLossClassification loss for observations not used for training
kfoldfunCross validate function
kfoldMarginClassification margins for observations not used for training
kfoldPredictPredict response for observations not used for training
lossClassification error
resubLossClassification error by resubstitution
logPLog unconditional probability density for discriminant analysis classifier
mahalMahalanobis distance to class means
nLinearCoeffsNumber of nonzero linear coefficients
compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge
marginClassification margins
resubEdgeClassification edge by resubstitution
resubMarginClassification margins by resubstitution
predictPredict labels using discriminant analysis classification model
resubPredictPredict resubstitution labels of discriminant analysis classification model
classifyDiscriminant analysis

ClassificationDiscriminantDiscriminant analysis classification
CompactClassificationDiscriminantCompact discriminant analysis class
ClassificationPartitionedModelCross-validated classification model

主题

Train Discriminant Analysis Classifiers Using Classification Learner App

Create and compare discriminant analysis classifiers, and export trained models to make predictions for new data.

Supervised Learning Workflow and Algorithms

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

Parametric Classification

Categorical response data

Discriminant Analysis Classification

Understand the discriminant analysis algorithm and how to fit a discriminant analysis model to data.

Creating Discriminant Analysis Model

Understand the algorithm used to construct discriminant analysis classifiers.

Create and Visualize Discriminant Analysis Classifier

Perform linear and quadratic classification of Fisher iris data.

Improving Discriminant Analysis Models

Examine and improve discriminant analysis model performance.

Regularize Discriminant Analysis Classifier

Make a more robust and simpler model by removing predictors without compromising the predictive power of the model.

Examine the Gaussian Mixture Assumption

Discriminant analysis assumes that the data comes from a Gaussian mixture model. Understand how to examine this assumption.

Prediction Using Discriminant Analysis Models

Understand how predict classifies observations using a discriminant analysis model.

Visualize Decision Surfaces of Different Classifiers

This example shows how to visualize the decision surface for different classification algorithms.