Interpretability
Train interpretable classification models and interpret complex classification models
Use inherently interpretable classification models, such as linear models, decision trees, and generalized additive models, or use interpretability features to interpret complex classification models that are not inherently interpretable.
To learn how to interpret classification models, see Interpret Machine Learning Models.
Functions
Objects
ClassificationGAM | Generalized additive model (GAM) for binary classification (Since R2021a) |
ClassificationLinear | Linear model for binary classification of high-dimensional data |
ClassificationTree | Binary decision tree for multiclass classification |
Topics
Model Interpretation
- Interpret Machine Learning Models
Explain model predictions using thelime
andshapley
objects and theplotPartialDependence
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 usingshapley
. - 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.
Interpretable Models
- 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.