# Classification on Zoo UCI (test) Source: [Classification on Zoo UCI (test)]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:58:12.190Z Evaluation of general classification performance on the test split of the Zoo UCI dataset, reporting standard metrics (Precision, Recall, F1) and pairwise competition results against various baseline classifiers. A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Classification | | Dataset (including any stated split) | Zoo UCI (test) | | Metric | Precision | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 8 of 8 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Precision](): Higher is better; unit: PERCENT. - [Recall](): Higher is better; unit: PERCENT. - [F1 Score](): Higher is better; unit: PERCENT. - [F1 Macro](): Higher is better; unit: PERCENT. - [Pairwise Competition (DT)](): Higher is better; unit: PERCENT. - [Pairwise Competition (RF)](): Higher is better; unit: PERCENT. - [Pairwise Competition (KNN)](): Higher is better; unit: PERCENT. - [Pairwise Competition (SVM)](): Higher is better; unit: PERCENT. - [Pairwise Competition (MNB)](): Higher is better; unit: PERCENT. - [Pairwise Competition (LR)](): Higher is better; unit: PERCENT. - [Pairwise Competition (STC-C)](): Higher is better; unit: PERCENT. - [Pairwise Competition (STC-Q)](): Higher is better; unit: PERCENT. - [Pairwise Competition (Mean)](): Higher is better; unit: PERCENT. ## Recorded results | Method | Precision | Result date | Report paper | | --- | --- | --- | --- | | Sparse Tensor Classifier Quantum (STC-Q) | 95.3 | Not recorded | 68fa3f1e1e209131ce8eb014 | | Support Vector Machine (SVM) | 94.6 | Not recorded | 68fa3f1e1e209131ce8eb014 | | Logistic Regression (LR) | 94.6 | Not recorded | 68fa3f1e1e209131ce8eb014 | | Decision Tree (DT) | 94.4 | Not recorded | 68fa3f1e1e209131ce8eb014 | | Random Forest (RF) | 93.6 | Not recorded | 68fa3f1e1e209131ce8eb014 | | K-Nearest Neighbors (KNN) | 91.6 | Not recorded | 68fa3f1e1e209131ce8eb014 | | Multinomial Naive Bayes (MNB) | 91.6 | Not recorded | 68fa3f1e1e209131ce8eb014 | | Sparse Tensor Classifier Classic (STC-C) | 79 | Not recorded | 68fa3f1e1e209131ce8eb014 | ### Sparse Tensor Classifier Quantum (STC-Q) Precision: 95.3. Result ID: 698b98397a967eddb76fdff6. [Reporting paper]() [Method origin paper]() [Implementation]() Recorded evaluation configuration: {"h":1,"b":1,"p":"1/2","probability":"quantum","runs":100}. This record does not assert independent reproduction. ### Support Vector Machine (SVM) Precision: 94.6. Result ID: 698b98397a967eddb76fdff2. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"implementation":"scikit-learn","tuning":"grid search on weighted f1-score","runs":100}. This record does not assert independent reproduction. ### Logistic Regression (LR) Precision: 94.6. Result ID: 698b98397a967eddb76fdff4. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"implementation":"scikit-learn","tuning":"grid search on weighted f1-score","runs":100}. This record does not assert independent reproduction. ### Decision Tree (DT) Precision: 94.4. Result ID: 698b98397a967eddb76fdfef. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"implementation":"scikit-learn","tuning":"grid search on weighted f1-score","runs":100}. This record does not assert independent reproduction. ### Random Forest (RF) Precision: 93.6. Result ID: 698b98397a967eddb76fdff0. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"implementation":"scikit-learn","tuning":"grid search on weighted f1-score","runs":100}. This record does not assert independent reproduction. ### K-Nearest Neighbors (KNN) Precision: 91.6. Result ID: 698b98397a967eddb76fdff1. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"implementation":"scikit-learn","tuning":"grid search on weighted f1-score","runs":100}. This record does not assert independent reproduction. ### Multinomial Naive Bayes (MNB) Precision: 91.6. Result ID: 698b98397a967eddb76fdff3. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"implementation":"scikit-learn","tuning":"grid search on weighted f1-score","runs":100}. This record does not assert independent reproduction. ### Sparse Tensor Classifier Classic (STC-C) Precision: 79. Result ID: 698b98397a967eddb76fdff5. [Reporting paper]() [Method origin paper]() [Implementation]() Recorded evaluation configuration: {"h":1,"b":0,"p":1,"probability":"classical","runs":100}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.