Export True and Predicted Class Labels from All Models in Classification Learner to CSV/Excel for Research Report

27 Juil 2025
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Mise à jour 19 Mai 2026
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I am using MATLAB's Classification Learner App to train multiple models (e.g., SVM, KNN, NN, etc.) on my dataset. The app displays the confusion matrix for each model individually, which is helpful.
For my research, I want to export the predicted class labels along with the true class labels for each trained model into a single .csv or .xlsx file — ideally including:
  • Model name
  • True class label
  • Predicted class label
How can I export these predictions for all models from Classification Learner into a table form from the classifier learner?

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dpb
dpb le 27 Juil 2025
Modifié(e) : dpb le 27 Juil 2025
That's the rub when using one of the prepackaged apps; you can only do with it what it is coded to do. The <export function documentation> tells you what that is and how to use it to export what it does export; other than that isn't available from the tool.
What, specifically, is exported is dependent at least somewhat upon the specific model; you would have to build a composite table of the data you want from the fields within that exported structure. There is no direct way to produce what you're asking for in that format.
But, undoubtedly what is returned from the app is the same structure that you would get if you trained the same model the same way directly so there isn't any other format from which to start.

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Drew
Drew le 19 Mai 2026 à 20:11
Modifié(e) : Drew le 19 Mai 2026 à 21:54
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The per-observation predictions can be extracted from a saved Classification Learner session file (`.mat`). The session file stores the validation and test predictions internally for all trained models. Two functions are attached which can export both validation and test predictions, just in slightly different formats. There is also a session file attached, to demonstrate the use of these functions.
On a related note, in R2026a, the ability to import and export from Classification Learner has been expanded. Trained models can be imported. Data partitions can be imported and exported. Plots, models, and other in-app data structures (such as a shapley explainer object) can be exported. Additional information can be obtained from the saved session, as in the workflow below.
Workflow:
  1. In Classification Learner, train your models (SVM, KNN, NN, etc.)
  2. Save the session: Save Session > Save Session As → `my_session.mat`
  3. Run one of the attached functions.
Here is an example using the attached saved R2026a Classification Learner session file which contains 36 models. The session file uses fisheriris data (150 observations), with 20 percent set aside for test, and 5-fold cross-validation on the remaining 80 percent.
% Stacked format as suggested in the question
[valResults, testResults] = exportClassificationLearnerPredictionsStacked("session_36models.mat")
Warning: This session was generated by the Classification Learner version of R2026a.
To resume the session, open the session file in Classification Learner.

Loaded session from: session_36models.mat Found 36 model(s). [1] Tree / Fine Tree - 120 validation predictions extracted. [2.1] Tree / Fine Tree - 120 validation predictions extracted. [2.2] Tree / Medium Tree - 120 validation predictions extracted. [2.3] Tree / Coarse Tree - 120 validation predictions extracted. [2.4] Linear Discriminant / Linear Discriminant - 120 validation predictions extracted. [2.5] Quadratic Discriminant / Quadratic Discriminant - 120 validation predictions extracted. [2.6] Efficient Logistic Regression / Efficient Logistic Regression - 120 validation predictions extracted. [2.7] Efficient Linear SVM / Efficient Linear SVM - 120 validation predictions extracted. [2.8] Naive Bayes / Gaussian Naive Bayes - 120 validation predictions extracted. [2.9] Naive Bayes / Kernel Naive Bayes - 120 validation predictions extracted. [2.10] SVM / Linear SVM - 120 validation predictions extracted. [2.11] SVM / Quadratic SVM - 120 validation predictions extracted. [2.12] SVM / Cubic SVM - 120 validation predictions extracted. [2.13] SVM / Fine Gaussian SVM - 120 validation predictions extracted. [2.14] SVM / Medium Gaussian SVM - 120 validation predictions extracted. [2.15] SVM / Coarse Gaussian SVM - 120 validation predictions extracted. [2.16] KNN / Fine KNN - 120 validation predictions extracted. [2.17] KNN / Medium KNN - 120 validation predictions extracted. [2.18] KNN / Coarse KNN - 120 validation predictions extracted. [2.19] KNN / Cosine KNN - 120 validation predictions extracted. [2.20] KNN / Cubic KNN - 120 validation predictions extracted. [2.21] KNN / Weighted KNN - 120 validation predictions extracted. [2.22] Kernel / SVM Kernel - 120 validation predictions extracted. [2.23] Kernel / Logistic Regression Kernel - 120 validation predictions extracted. [2.24] Ensemble / Boosted Trees - 120 validation predictions extracted. [2.25] Ensemble / Bagged Trees - 120 validation predictions extracted. [2.26] Ensemble / Subspace Discriminant - 120 validation predictions extracted. [2.27] Ensemble / Subspace KNN - 120 validation predictions extracted. [2.28] Ensemble / RUSBoosted Trees - 120 validation predictions extracted. [2.29] Neural Network / Narrow Neural Network - 120 validation predictions extracted. [2.30] Neural Network / Medium Neural Network - 120 validation predictions extracted. [2.31] Neural Network / Wide Neural Network - 120 validation predictions extracted. [2.32] Neural Network / Bilayered Neural Network - 120 validation predictions extracted. [2.33] Neural Network / Trilayered Neural Network - 120 validation predictions extracted. [2.34] Customizable Neural Network / Fully Connected Customizable Neural Network - 120 validation predictions extracted. [2.35] Customizable Neural Network / Residual Customizable Neural Network - 120 validation predictions extracted. [1] Tree / Fine Tree - 30 test predictions extracted. [2.1] Tree / Fine Tree - 30 test predictions extracted. [2.2] Tree / Medium Tree - 30 test predictions extracted. [2.3] Tree / Coarse Tree - 30 test predictions extracted. [2.4] Linear Discriminant / Linear Discriminant - 30 test predictions extracted. [2.5] Quadratic Discriminant / Quadratic Discriminant - 30 test predictions extracted. [2.6] Efficient Logistic Regression / Efficient Logistic Regression - 30 test predictions extracted. [2.7] Efficient Linear SVM / Efficient Linear SVM - 30 test predictions extracted. [2.8] Naive Bayes / Gaussian Naive Bayes - 30 test predictions extracted. [2.9] Naive Bayes / Kernel Naive Bayes - 30 test predictions extracted. [2.10] SVM / Linear SVM - 30 test predictions extracted. [2.11] SVM / Quadratic SVM - 30 test predictions extracted. [2.12] SVM / Cubic SVM - 30 test predictions extracted. [2.13] SVM / Fine Gaussian SVM - 30 test predictions extracted. [2.14] SVM / Medium Gaussian SVM - 30 test predictions extracted. [2.15] SVM / Coarse Gaussian SVM - 30 test predictions extracted. [2.16] KNN / Fine KNN - 30 test predictions extracted. [2.17] KNN / Medium KNN - 30 test predictions extracted. [2.18] KNN / Coarse KNN - 30 test predictions extracted. [2.19] KNN / Cosine KNN - 30 test predictions extracted. [2.20] KNN / Cubic KNN - 30 test predictions extracted. [2.21] KNN / Weighted KNN - 30 test predictions extracted. [2.22] Kernel / SVM Kernel - 30 test predictions extracted. [2.23] Kernel / Logistic Regression Kernel - 30 test predictions extracted. [2.24] Ensemble / Boosted Trees - 30 test predictions extracted. [2.25] Ensemble / Bagged Trees - 30 test predictions extracted. [2.26] Ensemble / Subspace Discriminant - 30 test predictions extracted. [2.27] Ensemble / Subspace KNN - 30 test predictions extracted. [2.28] Ensemble / RUSBoosted Trees - 30 test predictions extracted. [2.29] Neural Network / Narrow Neural Network - 30 test predictions extracted. [2.30] Neural Network / Medium Neural Network - 30 test predictions extracted. [2.31] Neural Network / Wide Neural Network - 30 test predictions extracted. [2.32] Neural Network / Bilayered Neural Network - 30 test predictions extracted. [2.33] Neural Network / Trilayered Neural Network - 30 test predictions extracted. [2.34] Customizable Neural Network / Fully Connected Customizable Neural Network - 30 test predictions extracted. [2.35] Customizable Neural Network / Residual Customizable Neural Network - 30 test predictions extracted.
valResults = 4320×6 table
ObservationIndex ModelNumber ModelType ModelPreset TrueLabel PredictedLabel ________________ ___________ _________ ___________ __________ ______________ 1 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 2 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 3 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 4 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 5 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 6 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 7 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 8 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 9 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 10 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 11 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 12 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 13 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 14 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 15 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'} 16 "1" "Tree" "Fine Tree" {'setosa'} {'setosa'}
testResults = 1080×6 table
ObservationIndex ModelNumber ModelType ModelPreset TrueLabel PredictedLabel ________________ ___________ _________ ___________ ______________ ______________ 1 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 2 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 3 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 4 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 5 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 6 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 7 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 8 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 9 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 10 "1" "Tree" "Fine Tree" {'setosa' } {'setosa' } 11 "1" "Tree" "Fine Tree" {'versicolor'} {'versicolor'} 12 "1" "Tree" "Fine Tree" {'versicolor'} {'versicolor'} 13 "1" "Tree" "Fine Tree" {'versicolor'} {'virginica' } 14 "1" "Tree" "Fine Tree" {'versicolor'} {'versicolor'} 15 "1" "Tree" "Fine Tree" {'versicolor'} {'versicolor'} 16 "1" "Tree" "Fine Tree" {'versicolor'} {'virginica' }
% Wide format with model results side-by-side
[valResults, testResults] = exportClassificationLearnerPredictionsWide("session_36models.mat")
Warning: This session was generated by the Classification Learner version of R2026a.
To resume the session, open the session file in Classification Learner.

Loaded session from: session_36models.mat Found 36 model(s). [1] Model 1 Prediction - 120 validation predictions extracted. [2.1] Model 2.1 Prediction - 120 validation predictions extracted. [2.2] Model 2.2 Prediction - 120 validation predictions extracted. [2.3] Model 2.3 Prediction - 120 validation predictions extracted. [2.4] Model 2.4 Prediction - 120 validation predictions extracted. [2.5] Model 2.5 Prediction - 120 validation predictions extracted. [2.6] Model 2.6 Prediction - 120 validation predictions extracted. [2.7] Model 2.7 Prediction - 120 validation predictions extracted. [2.8] Model 2.8 Prediction - 120 validation predictions extracted. [2.9] Model 2.9 Prediction - 120 validation predictions extracted. [2.10] Model 2.10 Prediction - 120 validation predictions extracted. [2.11] Model 2.11 Prediction - 120 validation predictions extracted. [2.12] Model 2.12 Prediction - 120 validation predictions extracted. [2.13] Model 2.13 Prediction - 120 validation predictions extracted. [2.14] Model 2.14 Prediction - 120 validation predictions extracted. [2.15] Model 2.15 Prediction - 120 validation predictions extracted. [2.16] Model 2.16 Prediction - 120 validation predictions extracted. [2.17] Model 2.17 Prediction - 120 validation predictions extracted. [2.18] Model 2.18 Prediction - 120 validation predictions extracted. [2.19] Model 2.19 Prediction - 120 validation predictions extracted. [2.20] Model 2.20 Prediction - 120 validation predictions extracted. [2.21] Model 2.21 Prediction - 120 validation predictions extracted. [2.22] Model 2.22 Prediction - 120 validation predictions extracted. [2.23] Model 2.23 Prediction - 120 validation predictions extracted. [2.24] Model 2.24 Prediction - 120 validation predictions extracted. [2.25] Model 2.25 Prediction - 120 validation predictions extracted. [2.26] Model 2.26 Prediction - 120 validation predictions extracted. [2.27] Model 2.27 Prediction - 120 validation predictions extracted. [2.28] Model 2.28 Prediction - 120 validation predictions extracted. [2.29] Model 2.29 Prediction - 120 validation predictions extracted. [2.30] Model 2.30 Prediction - 120 validation predictions extracted. [2.31] Model 2.31 Prediction - 120 validation predictions extracted. [2.32] Model 2.32 Prediction - 120 validation predictions extracted. [2.33] Model 2.33 Prediction - 120 validation predictions extracted. [2.34] Model 2.34 Prediction - 120 validation predictions extracted. [2.35] Model 2.35 Prediction - 120 validation predictions extracted. [1] Model 1 Prediction - 30 test predictions extracted. [2.1] Model 2.1 Prediction - 30 test predictions extracted. [2.2] Model 2.2 Prediction - 30 test predictions extracted. [2.3] Model 2.3 Prediction - 30 test predictions extracted. [2.4] Model 2.4 Prediction - 30 test predictions extracted. [2.5] Model 2.5 Prediction - 30 test predictions extracted. [2.6] Model 2.6 Prediction - 30 test predictions extracted. [2.7] Model 2.7 Prediction - 30 test predictions extracted. [2.8] Model 2.8 Prediction - 30 test predictions extracted. [2.9] Model 2.9 Prediction - 30 test predictions extracted. [2.10] Model 2.10 Prediction - 30 test predictions extracted. [2.11] Model 2.11 Prediction - 30 test predictions extracted. [2.12] Model 2.12 Prediction - 30 test predictions extracted. [2.13] Model 2.13 Prediction - 30 test predictions extracted. [2.14] Model 2.14 Prediction - 30 test predictions extracted. [2.15] Model 2.15 Prediction - 30 test predictions extracted. [2.16] Model 2.16 Prediction - 30 test predictions extracted. [2.17] Model 2.17 Prediction - 30 test predictions extracted. [2.18] Model 2.18 Prediction - 30 test predictions extracted. [2.19] Model 2.19 Prediction - 30 test predictions extracted. [2.20] Model 2.20 Prediction - 30 test predictions extracted. [2.21] Model 2.21 Prediction - 30 test predictions extracted. [2.22] Model 2.22 Prediction - 30 test predictions extracted. [2.23] Model 2.23 Prediction - 30 test predictions extracted. [2.24] Model 2.24 Prediction - 30 test predictions extracted. [2.25] Model 2.25 Prediction - 30 test predictions extracted. [2.26] Model 2.26 Prediction - 30 test predictions extracted. [2.27] Model 2.27 Prediction - 30 test predictions extracted. [2.28] Model 2.28 Prediction - 30 test predictions extracted. [2.29] Model 2.29 Prediction - 30 test predictions extracted. [2.30] Model 2.30 Prediction - 30 test predictions extracted. [2.31] Model 2.31 Prediction - 30 test predictions extracted. [2.32] Model 2.32 Prediction - 30 test predictions extracted. [2.33] Model 2.33 Prediction - 30 test predictions extracted. [2.34] Model 2.34 Prediction - 30 test predictions extracted. [2.35] Model 2.35 Prediction - 30 test predictions extracted.
valResults = 120×38 table
ObservationIndex TrueLabel Model 1 Prediction Model 2.1 Prediction Model 2.2 Prediction Model 2.3 Prediction Model 2.4 Prediction Model 2.5 Prediction Model 2.6 Prediction Model 2.7 Prediction Model 2.8 Prediction Model 2.9 Prediction Model 2.10 Prediction Model 2.11 Prediction Model 2.12 Prediction Model 2.13 Prediction Model 2.14 Prediction Model 2.15 Prediction Model 2.16 Prediction Model 2.17 Prediction Model 2.18 Prediction Model 2.19 Prediction Model 2.20 Prediction Model 2.21 Prediction Model 2.22 Prediction Model 2.23 Prediction Model 2.24 Prediction Model 2.25 Prediction Model 2.26 Prediction Model 2.27 Prediction Model 2.28 Prediction Model 2.29 Prediction Model 2.30 Prediction Model 2.31 Prediction Model 2.32 Prediction Model 2.33 Prediction Model 2.34 Prediction Model 2.35 Prediction ________________ __________ __________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ 1 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 2 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 3 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 4 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 5 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 6 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 7 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 8 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 9 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 10 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 11 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 12 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 13 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 14 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'virginica'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 15 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} 16 {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa' } {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'} {'setosa'}
testResults = 30×38 table
ObservationIndex TrueLabel Model 1 Prediction Model 2.1 Prediction Model 2.2 Prediction Model 2.3 Prediction Model 2.4 Prediction Model 2.5 Prediction Model 2.6 Prediction Model 2.7 Prediction Model 2.8 Prediction Model 2.9 Prediction Model 2.10 Prediction Model 2.11 Prediction Model 2.12 Prediction Model 2.13 Prediction Model 2.14 Prediction Model 2.15 Prediction Model 2.16 Prediction Model 2.17 Prediction Model 2.18 Prediction Model 2.19 Prediction Model 2.20 Prediction Model 2.21 Prediction Model 2.22 Prediction Model 2.23 Prediction Model 2.24 Prediction Model 2.25 Prediction Model 2.26 Prediction Model 2.27 Prediction Model 2.28 Prediction Model 2.29 Prediction Model 2.30 Prediction Model 2.31 Prediction Model 2.32 Prediction Model 2.33 Prediction Model 2.34 Prediction Model 2.35 Prediction ________________ ______________ __________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ ____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ _____________________ 1 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } 2 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } 3 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } 4 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'versicolor'} {'setosa' } {'setosa' } {'setosa' } 5 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } 6 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'versicolor'} {'setosa' } {'setosa' } {'setosa' } 7 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } 8 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'versicolor'} {'setosa' } {'setosa' } {'setosa' } 9 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } 10 {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa'} {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } {'setosa' } 11 {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} 12 {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} 13 {'versicolor'} {'virginica' } {'virginica' } {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'virginica' } {'versicolor'} {'virginica' } {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} 14 {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'virginica' } {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} 15 {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} {'versicolor'} 16 {'versicolor'} {'virginica' } {'virginica' } {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'versicolor'} {'virginica' } {'versicolor'} {'versicolor'} {'virginica' } {'virginica' } {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'virginica' } {'virginica' } {'versicolor'} {'versicolor'} {'setosa'} {'versicolor'} {'versicolor'} {'virginica' } {'setosa'} {'versicolor'} {'virginica' } {'virginica' } {'virginica' } {'virginica' } {'versicolor'} {'versicolor'}

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Muhammad
Muhammad
le 27 Juil 2025

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Drew
Drew
le 19 Mai 2026 à 21:54

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