  We introduce TabRepo, a new dataset of tabular model evaluations and
predictions. TabRepo contains the predictions and metrics of 1310 models
evaluated on 200 classification and regression datasets. We illustrate the
benefit of our dataset in multiple ways. First, we show that it allows to
perform analysis such as comparing Hyperparameter Optimization against current
AutoML systems while also considering ensembling at marginal cost by using
precomputed model predictions. Second, we show that our dataset can be readily
leveraged to perform transfer-learning. In particular, we show that applying
standard transfer-learning techniques allows to outperform current
state-of-the-art tabular systems in accuracy, runtime and latency.
