App Classification Learner

Entrene, valide y ajuste modelos de clasificación de forma interactiva

Elija entre distintos algoritmos para entrenar y validar modelos de clasificación para problemas binarios o multiclase. Tras entrenar varios modelos, compare los errores de validación de forma directa y, después, elija el mejor modelo. Para decidir qué algoritmo usar, consulte Train Classification Models in Classification Learner App.

Este diagrama de flujo muestra un flujo de trabajo frecuente para entrenar modelos de clasificación, o clasificadores, en la app Classification Learner.

Workflow in the Classification Learner app. Step 1: Select data and validation. Step 2: Choose classifier options. Step 3: Train a classifier. Step 4: Assess classifier performance. Step 5: Export the classifier.

Si desea hacer experimentos con uno de los modelos que ha entrenado en Classification Learner, puede exportar el modelo a la app Experiment Manager. Para obtener más información, consulte Export Model from Classification Learner to Experiment Manager.

Apps

Classification Learner	Entrenar modelos para clasificar datos usando machine learning supervisado
Experiment Manager	Design and run experiments to train and compare machine learning models (desde R2023a)

Temas

Flujo de trabajo frecuente

Train Classification Models in Classification Learner App
Workflow for training, comparing and improving classification models, including automated, manual, and parallel training.
Seleccionar datos para clasificación o abrir sesión guardada en la app
Importe datos en Classification Learner desde el espacio de trabajo o desde archivos, encuentre conjuntos de datos de ejemplo, elija opciones de validación cruzada o validación por retención y reserve datos para las pruebas. También puede abrir una sesión de la app previamente guardada.
Choose Classifier Options
In Classification Learner, automatically train a selection of models, or compare and tune options in decision tree, discriminant analysis, logistic regression, naive Bayes, support vector machine, nearest neighbor, kernel approximation, ensemble, and neural network models.
Visualizar y evaluar el rendimiento de clasificadores en Classification Learner
Compare los valores de precisión de los modelos, visualice resultados representando predicciones de clase y compruebe el rendimiento por clase en la matriz de confusión.
Export Classification Model to Predict New Data
After training in Classification Learner, export models to the workspace and Simulink^®, generate MATLAB^® code, generate C code for prediction, or export models for deployment to MATLAB Production Server™.
Train Decision Trees Using Classification Learner App
Create and compare classification trees, and export trained models to make predictions for new data.
Train Discriminant Analysis Classifiers Using Classification Learner App
Create and compare discriminant analysis classifiers, and export trained models to make predictions for new data.
Train Binary GLM Logistic Regression Classifier Using Classification Learner App
Create and compare binary logistic regression classifiers, and export trained models to make predictions for new data.
Train Naive Bayes Classifiers Using Classification Learner App
Create and compare naive Bayes classifiers, and export trained models to make predictions for new data.
Train Support Vector Machines Using Classification Learner App
Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data.
Train Nearest Neighbor Classifiers Using Classification Learner App
Create and compare nearest neighbor classifiers, and export trained models to make predictions for new data.
Train Kernel Approximation Classifiers Using Classification Learner App
Create and compare kernel approximation classifiers, and export trained models to make predictions for new data.
Train Ensemble Classifiers Using Classification Learner App
Create and compare ensemble classifiers, and export trained models to make predictions for new data.
Train Neural Network Classifiers Using Classification Learner App
Create and compare neural network classifiers, and export trained models to make predictions for new data.

Flujo de trabajo personalizado

Selección y transformación de características mediante la app Classification Learner
Identifique predictores útiles utilizando gráficas o algoritmos de clasificación de características, seleccione las características que desee incluir y transfórmelas con el PCA en Classification Learner.
Misclassification Costs in Classification Learner App
Before training any classification models, specify the costs associated with misclassifying the observations of one class into another.
Train and Compare Classifiers Using Misclassification Costs in Classification Learner App
Create classifiers after specifying misclassification costs, and compare the accuracy and total misclassification cost of the models.
Hyperparameter Optimization in Classification Learner App
Automatically tune hyperparameters of classification models by using hyperparameter optimization.
Train Classifier Using Hyperparameter Optimization in Classification Learner App
Train a classification support vector machine (SVM) model with optimized hyperparameters.
Check Classifier Performance Using Test Set in Classification Learner App
Import a test set into Classification Learner, and check the test set metrics for the best-performing trained models.
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.
Export Plots in Classification Learner App
Export and customize plots created before and after training.
Code Generation and Classification Learner App
Train a classification model using the Classification Learner app, and generate C/C++ code for prediction.
Code Generation for Binary GLM Logistic Regression Model Trained in Classification Learner
This example shows how to train a binary GLM logistic regression model using Classification Learner, and then generate C code that predicts labels using the exported classification model.
Deploy Model Trained in Classification Learner to MATLAB Production Server
Train a model in Classification Learner and export it for deployment to MATLAB Production Server.
Build Condition Model for Industrial Machinery and Manufacturing Processes
Train a binary classification model using Classification Learner App to detect anomalies in sensor data collected from an industrial manufacturing machine.

Flujo de trabajo de Experiment Manager

Export Model from Classification Learner to Experiment Manager
Export a classification model to Experiment Manager to perform multiple experiments.
Tune Classification Model Using Experiment Manager
Use different training data sets, hyperparameters, and visualizations to tune an efficient linear classifier in Experiment Manager.

Información relacionada

Machine learning en MATLAB
Gestionar experimentos (Deep Learning Toolbox)