Datasets:
Tasks:
Tabular Classification
Modalities:
Time-series
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English
Size:
1M - 10M
License:
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data
list | label
class label 3
classes |
|---|---|
[
1.9883499145507812,
-0.4792170524597168,
0.9462723731994629,
4.13084602355957,
1.8190970420837402
] | 22
|
[
1.559045672416687,
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0.5229737758636475,
4.482039451599121,
1.763649821281433
] | 22
|
[
2.6615092754364014,
0.4394832253456116,
0.9045349359512329,
4.431879043579102,
1.3374406099319458
] | 22
|
[
1.829937219619751,
0.9430307745933533,
0.06682609766721725,
4.086032867431641,
2.370414972305298
] | 22
|
[
0.5601386427879333,
0.7347556352615356,
2.3424761295318604,
4.305509090423584,
1.5601372718811035
] | 11
|
[
1.4777692556381226,
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1.7512670755386353
] | 11
|
[
3.2877357006073,
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] | 11
|
[
1.9961296319961548,
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1.2341053485870361
] | 00
|
[
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] | 11
|
[
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] | 11
|
[
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[
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] | 11
|
[
0.7935015559196472,
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] | 00
|
[
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|
[
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] | 00
|
[
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|
[
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|
[
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|
[
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] | 11
|
[
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] | 22
|
[
0.7978401184082031,
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] | 11
|
[
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] | 22
|
[
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] | 11
|
[
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] | 11
|
[
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] | 11
|
[
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] | 22
|
[
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] | 11
|
[
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] | 00
|
[
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] | 11
|
[
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] | 22
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[
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[
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|
[
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] | 11
|
[
1.357940435409546,
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] | 11
|
[
0.22707976400852203,
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|
[
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] | 22
|
[
1.6084463596343994,
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0.2223411649465561,
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] | 22
|
[
1.247273325920105,
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] | 11
|
[
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] | 00
|
[
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] | 00
|
[
1.7070403099060059,
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] | 11
|
[
1.5234591960906982,
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[
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|
[
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] | 22
|
[
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[
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] | 11
|
[
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] | 22
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[
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[
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] | 22
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[
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[
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[
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[
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] | 11
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[
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[
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[
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|
End of preview. Expand
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is_sparse/sparse5d
Dataset Description
This is a synthetic 5-dimensional classification dataset designed for sparse learning research. The dataset contains 3 classes and is specifically designed to have sparse optimal representations, where only a subset of features are informative for the classification task.
Dataset Summary
- Variant: sparse5d
- Features: 5 continuous features
- Classes: 3
- Entropy(Y): 1.4855
- Mutual Information (joint): 1.1819
- Maximum Achievable Accuracy: 0.8967
Dataset Structure
Data Instances
Each instance consists of:
data: A 5-dimensional feature vector (float32)label: An integer class label (0, 1, or 2)
Data Splits
| Split | Number of Instances |
|---|---|
| Train | Variable (see below) |
| Test | Variable (see below) |
Dataset Creation
This dataset was synthetically generated for research on sparse learning and optimal feature selection. The mutual information values between feature subsets and labels are provided in the metadata.
Mutual Information Structure
The dataset includes ground-truth mutual information values for various feature subsets, enabling:
- Feature importance analysis
- Information-theoretic learning algorithms
- Benchmarking of MI estimation methods
Key MI values:
- joint: 1.1819
- 1: 0.3273
- 1-2: 0.3273
- 1-2-3: 0.6634
- 1-2-3-4: 0.6634
- 1-2-3-4-5: 1.1819
- 1-2-3-5: 1.1819
- 1-2-4: 0.3273
- 1-2-4-5: 1.0492
- 1-2-5: 1.0492
Citation
If you use this dataset, please cite the associated research paper (to be added).
License
MIT License
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