Tip
In Classification Learner, tables are the easiest way to use your data,
because they can contain numeric and label data. Use the Import Tool to
bring your data into the MATLAB® workspace as a table, or use the table functions to create a
table from workspace variables. See Tables.
Load your data into the MATLAB workspace.
Predictor and response variables can be numeric, categorical, string, or logical vectors, cell arrays of character vectors, or character arrays. Note: If your response variable is a string vector, then the predictions of the trained model form a cell array of character vectors.
Combine the predictor data into one variable, either a table or a matrix. You can additionally combine your predictor data and response variable, or you can keep them separate.
For example data sets, see Example Data for Classification.
On the Apps tab, click Classification Learner.
In Classification Learner, on the Classification Learner tab, in the File section, click New Session > From Workspace.
In the New Session dialog box, under Data Set Variable, select a table or matrix from the list of workspace variables.
If you select a matrix, choose whether to use rows or columns for observations by clicking the option buttons.
Under Response, observe the default response variable. The app tries to select a suitable response variable from the data set variable and treats all other variables as predictors.
If you want to use a different response variable, you can:
Use the list to select another variable from the data set variable.
Select a separate workspace variable by clicking the From workspace option button and then selecting a variable from the list.
Under Predictors, add or remove predictors using the check boxes. Add or remove all predictors by clicking Add All or Remove All. You can also add or remove multiple predictors by selecting them in the table, and then clicking Add N or Remove N, where N is the number of selected predictors. The Add All and Remove All buttons change to Add N and Remove N when you select multiple predictors.
To accept the default validation scheme and continue, click Start Session. The default validation option is 5-fold cross-validation, which protects against overfitting.
Tip
If you have a large data set you might want to switch to holdout validation. To learn more, see Choose Validation Scheme.
For next steps, see Train Classification Models in Classification Learner App.
On the Classification Learner tab, in the File section, select New Session > From File.
Select a file type in the list, such as spreadsheets, text files, or comma
separated values (.csv) files, or select All
Files to browse for other file types such as
.dat.
To get started using Classification Learner, try the following example data sets.
| Name | Size | Description |
|---|---|---|
| Fisher Iris | Number of predictors: 4 | Measurements from three species of iris. Try to classify the species. For a step-by-step example, see Train Decision Trees Using Classification Learner App. |
Create a table from the
fishertable = readtable('fisheriris.csv');
| ||
| Credit Rating | Number of predictors: 6 | Financial ratios and industry sectors information for a list of corporate customers. The response variable consists of credit ratings (AAA, AA, A, BBB, BB, B, CCC) assigned by a rating agency. |
Create a table from the
creditrating = readtable('CreditRating_Historical.dat'); | ||
| Cars | Number of predictors: 7 | Measurements of cars, in 1970, 1976, and 1982. Try to classify the country of origin. |
Create a table from variables
in the load carsmall cartable = table(Acceleration, Cylinders, Displacement,... Horsepower, Model_Year, MPG, Weight, Origin); | ||
| Arrhythmia | Number of predictors: 279 | Patient information and response variables that indicate the presence and absence of cardiac arrhythmia. Misclassifying a patient as "normal" has more severe consequences than false positives classified as “has arrhythmia”. |
Create a table from the
load arrhythmia Arrhythmia = array2table(X); Arrhythmia.Class = categorical(Y); | ||
| Ovarian Cancer | Number of predictors: 4000 | Ovarian cancer data generated using the WCX2 protein array. Includes 95 controls and 121 ovarian cancers. |
Create a table from the
load ovariancancer ovariancancer = array2table(obs); ovariancancer.Group = categorical(grp); | ||
| Ionosphere | Number of predictors: 34 | Signals from a phased array of 16 high-frequency antennas. Good (“g”) returned radar signals are those showing evidence of some type of structure in the ionosphere. Bad (“b”) signals are those that pass through the ionosphere. |
Create a table from the
load ionosphere ionosphere = array2table(X); ionosphere.Group = Y; | ||
Choose a validation method to examine the predictive accuracy of the fitted models. Validation estimates model performance on new data compared to the training data, and helps you choose the best model. Validation protects against overfitting. Choose a validation scheme before training any models, so that you can compare all the models in your session using the same validation scheme.
Tip
Try the default validation scheme and click Start Session to continue. The default option is 5-fold cross-validation, which protects against overfitting.
If you have a large data set and training models takes too long using cross-validation, reimport your data and try the faster holdout validation instead.
Cross-Validation: Select a number of folds (or divisions) to partition the data set using the slider control.
If you choose k folds, then the app:
Partitions the data into k disjoint sets or folds
For each fold:
Trains a model using the out-of-fold observations
Assesses model performance using in-fold data
Calculates the average test error over all folds
This method gives a good estimate of the predictive accuracy of the final model trained with all the data. It requires multiple fits but makes efficient use of all the data, so it is recommended for small data sets.
Holdout Validation: Select a percentage of the data to use as a test set using the slider control. The app trains a model on the training set and assesses its performance with the test set. The model used for validation is based on only a portion of the data, so Holdout Validation is recommended only for large data sets. The final model is trained with the full data set.
No Validation: No protection against overfitting. The app uses all of the data for training and computes the error rate on the same data. Without any test data, you get an unrealistic estimate of the model’s performance on new data. That is, the training sample accuracy is likely to be unrealistically high, and the predictive accuracy is likely to be lower.
To help you avoid overfitting to the training data, choose a validation scheme instead.
Note
The validation scheme only affects the way that Classification Learner computes validation metrics. The final model is always trained using the full data set.
All the classification models you train after selecting data use the same validation scheme that you select in this dialog box. You can compare all the models in your session using the same validation scheme.
To change the validation selection and train new models, you can select data again, but you lose any trained models. The app warns you that importing data starts a new session. Save any trained models you want to keep to the workspace, and then import the data.
For next steps training models, see Train Classification Models in Classification Learner App.