La traducción de esta página aún no se ha actualizado a la versión más reciente. Haga clic aquí para ver la última versión en inglés.

Capacidad de interpretación

Entrene modelos de clasificación interpretables e interprete modelos de clasificación complejos

Emplee modelos de clasificación interpretables por naturaleza, por ejemplo, modelos lineales, árboles de decisión y modelos aditivos generalizados, o utilice las funcionalidades de interpretación para interpretar modelos de clasificación complejos que no son interpretables por naturaleza.

Para saber cómo interpretar modelos de clasificación, consulte Interpret Machine Learning Models.

Funciones

expandir todo

Interpretar un modelo entrenado

Explicaciones independientes del modelo local interpretable (LIME, por sus siglas en inglés)

`lime`	Local interpretable model-agnostic explanations (LIME) (desde R2020b)
`fit`	Fit simple model of local interpretable model-agnostic explanations (LIME) (desde R2020b)
`plot`	Plot results of local interpretable model-agnostic explanations (LIME) (desde R2020b)

Valores de Shapley

`shapley`	Shapley values (desde R2021a)
`fit`	Compute Shapley values for query points (desde R2021a)
`plot`	Plot Shapley values using bar graphs (desde R2021a)
`boxchart`	Visualize Shapley values using box charts (box plots) (desde R2024a)
`swarmchart`	Visualize Shapley values using swarm scatter charts (desde R2024a)

Dependencia parcial

`partialDependence`	Compute partial dependence (desde R2020b)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots

Entrenar un modelo interpretable

`fitcgam`	Fit generalized additive model (GAM) for binary classification (desde R2021a)
`fitclinear`	Fit binary linear classifier to high-dimensional data
`fitctree`	Fit binary decision tree for multiclass classification

Objetos

`ClassificationGAM`	Generalized additive model (GAM) for binary classification (desde R2021a)
`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationTree`	Binary decision tree for multiclass classification

Temas

Interpretación de modelos

Interpret Machine Learning Models
Explain model predictions using the lime and shapley objects and the plotPartialDependence function.
Shapley Values for Machine Learning Model
Compute Shapley values for a machine learning model using interventional algorithm or conditional algorithm.
Shapley Output Functions
Stop Shapley computations, create plots, save information to your workspace, or perform calculations while using shapley.
Introduction to Feature Selection
Learn about feature selection algorithms and explore the functions available for feature selection.
Explain Model Predictions for Classifiers Trained in Classification Learner App
To understand how trained classifiers use predictors to make predictions, use global and local interpretability tools, such as partial dependence plots, LIME values, and Shapley values.
Use Partial Dependence Plots to Interpret Classifiers Trained in Classification Learner App
Determine how features are used in trained classifiers by creating partial dependence plots.

Modelos interpretables

Train Generalized Additive Model for Binary Classification
Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.
Train Decision Trees Using Classification Learner App
Create and compare classification trees, and export trained models to make predictions for new data.
Classification Using Nearest Neighbors
Categorize data points based on their distance to points in a training data set, using a variety of distance metrics.