First-Order Theorem Proving: Subsampled Logic Dataset with 10 Classes and 100 Features

first-order-theorem-proving_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True is a subsampled version of the first-order-theorem-proving dataset from OpenML. It was generated by Eddie Bergman using a script to uniformly sample rows, columns, and classes. The dataset likely contains tabular data related to automated theorem proving tasks.

Use Cases

• Benchmarking classification algorithms based on the 10 target classes mentioned in the description.
• Feature selection experiments based on the 100 sampled columns.
• Evaluating model performance on stratified subsets of logic data.
• Studying the relationship between logical features and theorem proving outcomes.

Strengths

• Subsampling parameters are explicitly defined: seed=1, nrows_max=2000, ncols_max=100, nclasses_max=10.
• The generation code indicates stratified sampling was used to preserve class distribution.
• Derived from a known OpenML dataset (ID 1475) focused on automated reasoning.

Limitations

• Row count is unknown; the actual size of the downloaded data may differ from the nrows_max parameter.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

OpenML, derived from dataset ID 1475 (first-order-theorem-proving).

Collection Method

Algorithmic subsampling of an existing dataset using a Python script.