car_acceptabilityOpenML #40975 ↗Car
The Car Evaluation dataset comprises 1,728 vehicle profiles evaluated across 6 categorical attributes covering price, maintenance, and technical capacity. It serves as a canonical multi-class classification benchmark to predict overall vehicle acceptability across four discrete tiers.
Business Objective: Car
01Business Context
Automotive dealerships, fleet procurement managers, and appraisal platforms evaluate multi-attribute vehicle trade-ins and inventory specifications to determine market readiness, customer appeal, and valuation tiers.
02Analytical Objective
Accurately classify vehicle acceptability into four discrete market categories (unacceptable, acceptable, good, very good) based on physical attributes, operating costs, and safety ratings.
03Economic & Decision Impact
Optimizes inventory acquisition margins and turnaround times. Misclassifying an unacceptable vehicle as acceptable (false positive) introduces unsellable, high-depreciation inventory, while under-rating premium vehicles (false negative) forfeits competitive deal volume and customer satisfaction.
Due to its deterministic hierarchical rule origins from the DEX expert system, tree-based algorithms (Random Forest, LightGBM, Decision Trees) achieve near 98–100% accuracy once ordinal encodings are recognized. Tabular foundation models like TabICL on Carla HQ replicate these intricate non-linear decision boundaries zero-shot directly in spreadsheet environments without requiring explicit feature engineering or pipeline tuning.
Target & Feature Column Definitions
Exhaustive business definitions, measurement units, roles, and target variables across all 7 columns.
| Col | Variable / Feature Name | Role | Type | Unit / Scale | Description & Business Meaning |
|---|---|---|---|---|---|
| A | car_acceptabilityAcceptability Rating | TARGET | CAT | — | Target categorical acceptability rating: unacc (unacceptable), acc (acceptable), good (good), vgood (very good). |
| B | buying_price_tierBuying Price Tier | Feature | CAT | — | Relative purchase cost level of the vehicle (vhigh: very high, high: high, med: medium, low: low). |
| C | maintenance_cost_tierMaintenance Cost Tier | Feature | CAT | — | Relative ongoing maintenance and service expense level (vhigh: very high, high: high, med: medium, low: low). |
| D | door_countNumber of Doors | Feature | CAT | doors | Categorical door count configuration of the vehicle (2, 3, 4, 5more). |
| E | passenger_capacityPassenger Capacity | Feature | CAT | passengers | Maximum passenger seating capacity category (2, 4, more). |
| F | luggage_boot_sizeLuggage Boot Size | Feature | CAT | — | Trunk and cargo storage capacity classification (small, med, big). |
| G | safety_ratingEstimated Safety Level | Feature | CAT | — | Evaluated passenger safety level category (low, med, high). |
Interactive Data Table
Explore rows, feature values, and target labels for Car.
TabICLv2 WebGPU Benchmark Results
Complete performance metrics on persisted IID or non-IID evaluation splits, executed 100% locally in the browser sandbox.
| Evaluation Split | Train Rows | Test Rows | Split Duration?Wall-clock inference time taken for this evaluation split running 100% locally via WebGPU.Learn more → | Accuracy?Proportion of correct test predictions across all classes.Google ML Guide & Details → | ROC-AUC?Area under the ROC curve evaluating ranking capability across all thresholds.Google ML Guide & Details → | F1 Score?Harmonic mean of Precision and Recall, robust against class imbalance.Learn more → | Precision?Proportion of predicted positives that were actually positive.Google ML Guide & Details → | Recall?Proportion of actual positive cases successfully captured.Google ML Guide & Details → |
|---|---|---|---|---|---|---|---|---|
| R1 / F1 | 1,152 | 576 | 1.47s | 99.48% | 0.9991 | 0.9362 | 0.9167 | 0.9565 |
| R1 / F2 | 1,152 | 576 | 1.46s | 99.13% | 0.9987 | 0.9020 | 0.8214 | 1.0000 |
| R1 / F3 | 1,152 | 576 | 1.46s | 99.48% | 0.9987 | 0.9362 | 0.9167 | 0.9565 |
| R2 / F1 | 1,152 | 576 | 1.45s | 98.61% | 0.9969 | 0.8400 | 0.7778 | 0.9130 |
| R2 / F2 | 1,152 | 576 | 1.47s | 98.78% | 0.9980 | 0.8511 | 0.8333 | 0.8696 |
| R2 / F3 | 1,152 | 576 | 1.46s | 98.96% | 0.9978 | 0.8750 | 0.8400 | 0.9130 |
| R3 / F1 | 1,152 | 576 | 1.47s | 99.48% | 0.9997 | 0.9333 | 0.9545 | 0.9130 |
| R3 / F2 | 1,152 | 576 | 1.47s | 98.09% | 0.9940 | 0.7843 | 0.7143 | 0.8696 |
| R3 / F3 | 1,152 | 576 | 1.47s | 99.48% | 1.0000 | 0.9388 | 0.8846 | 1.0000 |
| R4 / F1 | 1,152 | 576 | 1.47s | 99.48% | 0.9986 | 0.9388 | 0.8846 | 1.0000 |
| R4 / F2 | 1,152 | 576 | 1.46s | 98.96% | 0.9975 | 0.8800 | 0.8148 | 0.9565 |
| R4 / F3 | 1,152 | 576 | 1.46s | 97.74% | 0.9935 | 0.7111 | 0.7273 | 0.6957 |
| R5 / F1 | 1,152 | 576 | 1.48s | 99.48% | 0.9987 | 0.9388 | 0.8846 | 1.0000 |
| R5 / F2 | 1,152 | 576 | 1.52s | 98.26% | 0.9961 | 0.8000 | 0.7407 | 0.8696 |
| R5 / F3 | 1,152 | 576 | 1.48s | 99.83% | 1.0000 | 0.9778 | 1.0000 | 0.9565 |
| R6 / F1 | 1,152 | 576 | 1.46s | 99.31% | 0.9991 | 0.9167 | 0.8800 | 0.9565 |
| R6 / F2 | 1,152 | 576 | 1.54s | 98.96% | 0.9974 | 0.8800 | 0.8148 | 0.9565 |
| R6 / F3 | 1,152 | 576 | 1.48s | 98.61% | 0.9973 | 0.8000 | 0.9412 | 0.6957 |
| R7 / F1 | 1,152 | 576 | 1.47s | 98.78% | 0.9954 | 0.8679 | 0.7667 | 1.0000 |
| R7 / F2 | 1,152 | 576 | 1.47s | 99.48% | 0.9992 | 0.9333 | 0.9545 | 0.9130 |
| R7 / F3 | 1,152 | 576 | 1.48s | 99.31% | 0.9991 | 0.9130 | 0.9130 | 0.9130 |
| R8 / F1 | 1,152 | 576 | 1.51s | 98.96% | 0.9987 | 0.8846 | 0.7931 | 1.0000 |
| R8 / F2 | 1,152 | 576 | 1.47s | 98.61% | 0.9972 | 0.8333 | 0.8000 | 0.8696 |
| R8 / F3 | 1,152 | 576 | 1.48s | 98.78% | 0.9987 | 0.8372 | 0.9000 | 0.7826 |
| R9 / F1 | 1,152 | 576 | 1.47s | 99.48% | 0.9992 | 0.9388 | 0.8846 | 1.0000 |
| R9 / F2 | 1,152 | 576 | 1.47s | 98.78% | 0.9976 | 0.8444 | 0.8636 | 0.8261 |
| R9 / F3 | 1,152 | 576 | 1.48s | 99.31% | 0.9986 | 0.9130 | 0.9130 | 0.9130 |
| R10 / F1 | 1,152 | 576 | 1.45s | 99.31% | 0.9992 | 0.9130 | 0.9130 | 0.9130 |
| R10 / F2 | 1,152 | 576 | 1.48s | 98.96% | 0.9981 | 0.8750 | 0.8400 | 0.9130 |
| R10 / F3 | 1,152 | 576 | 1.48s | 98.61% | 0.9976 | 0.8182 | 0.8571 | 0.7826 |
| Mean ± Std | — | — | 1.48s | 99.02% ± 0.47% | 0.998 ± 0.002 | 0.880 ± 0.059 | 0.858 | 0.910 |
Foundation Models vs Traditional ML Baselines
Side-by-side evaluation of our in-browser Carla engine (WebGPU), open-source tabular foundation models, and OpenML baselines.
| Rank | Algorithm / Model | Model Family | Runtime | Predictive Accuracy?Proportion of correct test predictions across cross-validation splits.Google ML Guide & Details → | ROC-AUC?Area under the ROC curve evaluating ranking capability.Learn more → | F-Measure?Harmonic mean of precision and recall.Learn more → | Reference / Repo |
|---|---|---|---|---|---|---|---|
| #1 | pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC) | Support Vector Machine (SVM) | Python | 100.00% | 1.0000 | 1.0000 | OpenML #9202815 ↗ |
| #2 | MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic))) | Support Vector Machine (SVM) | Python | 100.00% | 0.9992 | 1.0000 | OpenML #10416489 ↗ |
| #3 | pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC) | Support Vector Machine (SVM) | Python | 100.00% | 1.0000 | 1.0000 | OpenML #9011260 ↗ |
| #4 | Google TabFM v1.0 | Tabular Foundation Model | PyTorch/Python | 99.65% | 0.9995 | 0.9569 | google-research/tabfm ↗ |
| #5 | pipeline.Pipeline(columntransformer=sklearn.compose. column transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute. base.SimpleImputer,onehotencoder=sklearn.preprocessing. encoders.OneHotEncoder)),variancethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,mlpclassifier=sklearn.neural network.multilayer perceptron.MLPClassifier) | Neural Network (MLP) | Python | 99.02% | 0.9998 | 0.9902 | OpenML #10418653 ↗ |
| #6 | Carla Engine (TabICLv2 WebGPU) | Tabular Foundation Model | Browser | 99.02% | 0.9980 | 0.8804 | 100% In-Browser |
| #7 | Streaming nanotabicl | Tabular Foundation Model | PyTorch/Python | 98.98% | 0.9980 | 0.8769 | soda-inria/nanotabicl ↗ |
| #8 | Full TabICLv2 (PyTorch Reference) | Tabular Foundation Model | PyTorch/Python | 98.98% | 0.9980 | 0.8763 | soda-inria/tabicl ↗ |
| #9 | nanotabicl Vanilla | Tabular Foundation Model | PyTorch/Python | 98.96% | 0.9980 | 0.8745 | soda-inria/nanotabicl ↗ |
| #10 | TabPFN v3 | Tabular Foundation Model | PyTorch/Python | 98.96% | 0.9980 | 0.8703 | PriorLabs/tabpfn ↗ |
| #11 | pipeline.Pipeline(columntransformer=sklearn.compose. column transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute. base.SimpleImputer,onehotencoder=sklearn.preprocessing. encoders.OneHotEncoder)),variancethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient boosting.GradientBoostingClassifier) | Gradient Boosting | Python | 98.44% | 0.9994 | 0.9844 | OpenML #10418598 ↗ |
| #12 | EXAONE Tabular | Tabular Foundation Model | PyTorch/Python | 98.42% | 0.9960 | 0.7987 | LGAI-Research/EXAONE-Tabular ↗ |
| #13 | LogitBoost(weka.classifiers.trees.REPTree) | Logistic / Linear Model | Python | 98.09% | 0.9991 | 0.9809 | OpenML #10416559 ↗ |
| #14 | pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier) | Random Forest | Python | 98.03% | 0.9995 | 0.9805 | OpenML #9918577 ↗ |
| #15 | pipeline.Pipeline(columntransformer=sklearn.compose. column transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute. base.SimpleImputer,onehotencoder=sklearn.preprocessing. encoders.OneHotEncoder)),variancethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) | Decision Tree | Python | 97.92% | 0.9807 | 0.9791 | OpenML #10417868 ↗ |
| #16 | pipeline.Pipeline(columntransformer=sklearn.compose. column transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute. base.SimpleImputer,onehotencoder=sklearn.preprocessing. encoders.OneHotEncoder)),variancethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) | Decision Tree | Python | 97.92% | 0.9807 | 0.9791 | OpenML #10418209 ↗ |
| #17 | pipeline.Pipeline(columntransformer=sklearn.compose. column transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute. base.SimpleImputer,onehotencoder=sklearn.preprocessing. encoders.OneHotEncoder)),variancethreshold=sklearn.feature selection.variance threshold.VarianceThreshold,adaboostclassifier=sklearn.ensemble.weight boosting.AdaBoostClassifier(base estimator=sklearn.tree.tree.DecisionTreeClassifier)) | AdaBoost | Python | 97.57% | 0.9768 | 0.9756 | OpenML #10417921 ↗ |
Variable Schema & Summary Distributions
Observed numerical ranges, category cardinalities, missing rates, and sample values across 1,728 rows.
| Col | Variable Name | Type | Missing | Distinct | Summary Stats / Distribution | Sample Values |
|---|---|---|---|---|---|---|
| A | car_acceptabilityTARGET | CAT | 0 | 4 | 4 cats: unacc, acc, good +1 more | unaccunaccunacc |
| B | buying_price_tier | CAT | 0 | 4 | 4 cats: vhigh, high, med +1 more | vhighvhighvhigh |
| C | maintenance_cost_tier | CAT | 0 | 4 | 4 cats: vhigh, high, med +1 more | vhighvhighvhigh |
| D | door_count | CAT | 0 | 4 | 4 cats: 2, 3, 4 +1 more | 222 |
| E | passenger_capacity | CAT | 0 | 3 | 3 cats: 2, 4, more | 222 |
| F | luggage_boot_size | CAT | 0 | 3 | 3 cats: small, med, big | smallsmallsmall |
| G | safety_rating | CAT | 0 | 3 | 3 cats: low, med, high | lowmedhigh |
Frequently Asked Questions: Car
Common questions regarding the Car dataset, machine learning task formulations, and in-browser tabular inference.
What is the Car Evaluation dataset used for?
The Car Evaluation dataset is a standard benchmark used to test multi-class categorical classification, constructive induction, and decision tree algorithms by predicting overall vehicle acceptability based on six structural and economic attributes.
Can I run zero-server predictions on this dataset in Google Sheets?
Yes, Carla HQ enables zero-server in-context tabular prediction directly in spreadsheets using TabICL foundation models without needing local Python environments or backend deployments.
What machine learning models perform best on the Car Evaluation dataset?
Decision tree-based algorithms (such as Random Forest, XGBoost, and CatBoost) and tabular foundation models like TabICL excel on this dataset, consistently reaching 98% to 100% accuracy due to the clear underlying hierarchical logic.
Test TabICLv2 on Car Yourself
Open the pre-loaded Google Sheet and let Carla configure the target and task for local, zero-cloud tabular machine learning.
Launch Carla
Click once to open the spreadsheet and Carla side panel together.
Review the Setup
Carla selects the dataset target and task from this page automatically.
Evaluate & Predict
Run predictions and compute metrics with zero server uploads.
Provenance & Attribution
OpenML Dataset 40975