plot
Description
plot( creates a bar graph of the
accuracy (for classification models) or mean squared error (for regression models) for the
data slices in sliceResults)sliceResults. For most metrics,
plot displays bars for the data slices and their complements.
The complement of a data slice consists of all observations that are not in the data
slice.
plot(
specifies the slice metric to display in the bar graph.sliceResults,Metric=metric)
b = plot(___)Bar objects using any of the input argument combinations in previous
syntaxes. Use b to query or modify Bar Properties after displaying the bar graph.
Examples
Train a binary classifier on numeric data. Use sliceMetrics to slice the training data according to one of the predictors. Evaluate the accuracy of the model predictions on the data slices.
Load the sample file fisheriris.csv, which contains iris data including sepal length, sepal width, petal width, and species type. Read the file into a table, and then convert the Species variable into a categorical variable. Display the first eight observations in the table.
fisheriris = readtable("fisheriris.csv");
fisheriris.Species = categorical(fisheriris.Species);
head(fisheriris) SepalLength SepalWidth PetalLength PetalWidth Species
___________ __________ ___________ __________ _______
5.1 3.5 1.4 0.2 setosa
4.9 3 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5 3.4 1.5 0.2 setosa
Separate the data for two of the iris species: versicolor and virginica.
versicolorData = fisheriris(fisheriris.Species=="versicolor",:); virginicaData = fisheriris(fisheriris.Species=="virginica",:); trainingData = [versicolorData;virginicaData];
Train a binary tree classifier on the versicolor and virginica data.
Mdl = fitctree(trainingData,"Species")Mdl =
ClassificationTree
PredictorNames: {'SepalLength' 'SepalWidth' 'PetalLength' 'PetalWidth'}
ResponseName: 'Species'
CategoricalPredictors: []
ClassNames: [versicolor virginica]
ScoreTransform: 'none'
NumObservations: 100
Properties, Methods
Mdl is a ClassificationTree model object trained on 100 observations.
Compute metrics on the training data slices determined by petal length. Because PetalLength is a numeric predictor, sliceMetrics creates data slices by binning the petal length values of observations in Mdl.X. Data slices always partition the data.
sliceResults = sliceMetrics(Mdl,"PetalLength")sliceResults =
sliceMetrics evaluated on PetalLength slices:
PetalLength NumObservations Accuracy OddsRatio PValue EffectSize
___________ _______________ ________ _________ ________ __________
[3, 3.65) 6 1 0 0.083246 -0.031915
[3.65, 4.3) 17 1 0 0.083241 -0.036145
[4.3, 4.95) 31 0.96774 1.1167 0.93193 0.0032726
[4.95, 5.6) 21 0.90476 8.2105 0.23003 0.08258
[5.6, 6.25) 19 1 0 0.08324 -0.037037
[6.25, 6.9] 6 1 0 0.083246 -0.031915
Properties, Methods
In this example, sliceMetrics creates six data slices. The accuracy for each data slice is quite high (over 90%).
Visualize the accuracy for each slice and its complement in Mdl.X.
plot(sliceResults)
In general, the accuracy for each slice is similar to the accuracy for its slice complement. However, the observations with petal lengths in the range [4.95,5.6) have a slightly lower percentage of correct classifications than all other observations.
Train a regression model on a mix of numeric and categorical data. Use sliceMetrics to slice the test data according to two predictors. Compute the mean squared error for the data slices. To improve the general model performance across the data slices, generate synthetic observations and use them to retrain the model.
Load the carbig data set, which contains measurements of cars made in the 1970s and early 1980s. Bin the Model_Year data to form a categorical variable, and combine the variable with a subset of the other measurements into a table. Remove observations with missing values from the table. Then, display the first eight observations in the table.
load carbig ModelDecade = discretize(Model_Year,[70 80 89], ... "categorical",["70s","80s"]); ModelDecade = categorical(ModelDecade,Ordinal=false); cars = table(Acceleration,Displacement,Horsepower, ... ModelDecade,Weight,MPG); cars = rmmissing(cars); head(cars)
Acceleration Displacement Horsepower ModelDecade Weight MPG
____________ ____________ __________ ___________ ______ ___
12 307 130 70s 3504 18
11.5 350 165 70s 3693 15
11 318 150 70s 3436 18
12 304 150 70s 3433 16
10.5 302 140 70s 3449 17
10 429 198 70s 4341 15
9 454 220 70s 4354 14
8.5 440 215 70s 4312 14
Partition the data into training data and test data. Reserve approximately 50% of the observations for computing slice metrics, and use the rest of the observations for model training.
rng(0,"twister") % For reproducibility of partition cv = cvpartition(length(cars.MPG),Holdout=0.5); trainingCars = cars(training(cv),:); testCars = cars(test(cv),:);
Train a regression tree model using the training data. Then, compute metrics on the test data slices determined by the decade of manufacture and the weight of the car. Partition the numeric Weight values into three bins. Because ModelDecade is a categorical variable with two categories, sliceMetrics creates six data slices.
Mdl = fitrtree(trainingCars,"MPG"); sliceResults = sliceMetrics(Mdl,testCars,["ModelDecade","Weight"], ... NumBins=3)
sliceResults =
sliceMetrics evaluated on ModelDecade and Weight slices:
ModelDecade Weight NumObservations Error TStatistic PValue EffectSize
___________ ________________ _______________ ______ __________ __________ __________
70s [1649, 2812.7) 64 18.392 0.23812 0.81224 1.3097
70s [2812.7, 3976.3) 53 6.6804 -4.3456 2.3745e-05 -14.843
70s [3976.3, 5140] 36 5.6089 -4.7484 4.0884e-06 -14.578
80s [1649, 2812.7) 32 30.874 2.2891 0.02727 15.972
80s [2812.7, 3976.3) 11 64.626 2.4672 0.032784 49.918
80s [3976.3, 5140] 0 NaN NaN NaN NaN
Properties, Methods
For the cars made in the 80s with a weight in the range [2812.7,3976.3) (that is, the observations in slice 5), the mean squared error (MSE) is much higher than the MSE for the cars in the other data slices.
Generate 500 synthetic observations using the synthesizeTabularData function. By default, the function uses a binning technique to learn the distribution of the variables in trainingCars before synthesizing the data.
rng(10,"twister") % For reproducibility of data generation syntheticCars = synthesizeTabularData(trainingCars,500);
Combine the training observations with the synthetic observations. Use the combined data to retrain the regression tree model.
newTrainingCars = [trainingCars;syntheticCars];
newMdl = fitrtree(newTrainingCars,"MPG");Compute metrics on the same test data slices using the retrained model.
newSliceResults = sliceMetrics(newMdl,testCars,["ModelDecade","Weight"], ... NumBins=3)
newSliceResults =
sliceMetrics evaluated on ModelDecade and Weight slices:
ModelDecade Weight NumObservations Error TStatistic PValue EffectSize
___________ ________________ _______________ ______ __________ __________ __________
70s [1649, 2812.7) 64 20.986 1.5012 0.13736 8.5476
70s [2812.7, 3976.3) 53 9.9331 -2.0166 0.045209 -7.2595
70s [3976.3, 5140] 36 5.4484 -4.3062 2.6575e-05 -11.982
80s [1649, 2812.7) 32 20.422 1.0664 0.29206 6.206
80s [2812.7, 3976.3) 11 24.162 1.0643 0.30927 9.463
80s [3976.3, 5140] 0 NaN NaN NaN NaN
Properties, Methods
Visually compare the MSE values for the data slices.
tiledlayout(1,2) nexttile plot(sliceResults) ylim([0 70]) xticklabels(1:6) legend(Location="northwest") nexttile plot(newSliceResults) ylim([0 70]) xticklabels(1:6) legend(Location="northwest")
The MSE for slice 5 is lower for the model trained with the training and synthetic observations (newMdl) than for the model trained with only the training data (Mdl).
Input Arguments
References
[1] Chung, Yeounoh, Tim Kraska, Neoklis Polyzotis, Ki Hyun Tae, and Steven Euijong Whang. “Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach.” IEEE Transactions on Knowledge and Data Engineering 32, no. 12 (2020): 2284–96.
Version History
Introduced in R2026a
