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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

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

limeLocal interpretable model-agnostic explanations (LIME)
fitFit simple model of local interpretable model-agnostic explanations (LIME)
plotPlot results of local interpretable model-agnostic explanations (LIME)

Valores de Shapley

shapleyShapley values
fitCompute Shapley values for query point
plotPlot Shapley values

Dependencia parcial

partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
fitcgamFit generalized additive model (GAM) for binary classification
fitclinearFit binary linear classifier to high-dimensional data
fitctreeFit binary decision tree for multiclass classification

Objetos

ClassificationGAMGeneralized additive model (GAM) for binary classification
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationTreeBinary decision tree for multiclass classification

Temas

Interpretación de modelos

Interpret Machine Learning Models

Explain model predictions using lime, shapley, and plotPartialDependence.

Shapley Values for Machine Learning Model

Compute Shapley values for a machine learning model using two algorithms: kernelSHAP and the extension to kernelSHAP.

Introduction to Feature Selection

Learn about feature selection algorithms and explore the functions available for feature selection.

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.