spb/wp2_uqo

1% Author: Simon-Pierre Boucher — contact@spboucher.ai2% Generated by scripts/08_revision_tables.py — do not edit by hand.3\begin{table}[!htbp]4\centering5\caption{Out-of-sample performance: random and spatial five-fold cross-validation.}6\label{tab:oos}7\small8\begin{adjustbox}{max width=\textwidth}9\begin{tabular}{lR{1.3cm}R{1.3cm}R{1.4cm}R{1.3cm}R{1.3cm}R{1.4cm}}10\toprule11& \multicolumn{3}{c}{\textbf{Random folds}} & \multicolumn{3}{c}{\textbf{FSA-grouped folds}} \\12\cmidrule(lr){2-4} \cmidrule(lr){5-7}13\textbf{Specification} & $\boldsymbol{R^2_{\text{oos}}}$ & \textbf{RMSE} & \textbf{MdAPE} & $\boldsymbol{R^2_{\text{oos}}}$ & \textbf{RMSE} & \textbf{MdAPE} \\14\midrule15A, raw lot-size field (for reference) & $-$1.036 & 1.003 & 27.5 & $-$1.309 & 1.068 & 27.6 \\16A: structural & 0.480 & 0.507 & 27.3 & 0.475 & 0.509 & 27.3 \\17B: A + description length & 0.489 & 0.502 & 27.1 & 0.484 & 0.505 & 27.3 \\18B-L: B + language indicators & 0.523 & 0.485 & 26.9 & 0.518 & 0.488 & 27.0 \\19D: B + 20 similarities & 0.528 & 0.483 & 26.9 & 0.522 & 0.486 & 27.0 \\20D-L: D + language indicators & 0.534 & 0.480 & 26.6 & 0.528 & 0.483 & 26.7 \\21B + 20 principal components of the embeddings & 0.544 & 0.475 & 26.5 & 0.537 & 0.478 & 26.7 \\22B-L + 20 principal components & 0.544 & 0.475 & 26.5 & 0.537 & 0.478 & 26.7 \\23ML-D: B + 20 multilingual similarities & 0.534 & 0.480 & 26.8 & 0.528 & 0.483 & 26.8 \\24ML-D-L: B-L + 20 multilingual similarities & 0.551 & 0.471 & 26.4 & 0.545 & 0.474 & 26.5 \\25\bottomrule26\end{tabular}27\end{adjustbox}28\begin{minipage}{\textwidth}29\vspace{4pt}\footnotesize30\textit{Notes:} Five-fold cross-validation on the $n = 17{,}087$ listings; predictions for each fold are made from a model estimated on the other four folds, and the metrics are computed on the pooled out-of-fold predictions (Equations~\ref{eq:r2oos}--\ref{eq:mdape}). Random folds are drawn with seed 42; FSA-grouped folds hold out entire forward sortation areas, so that no held-out listing shares an FSA with the training data. RMSE is in log points; MdAPE is the median absolute percentage error of the price level. Except for the first row, the lot-size variable is the unit-corrected log lot area with a missing indicator; the raw field (mixed units, maximum $7.2 \times 10^8$) yields exploding out-of-fold predictions and a negative $R^2$. Principal components are estimated on the training folds only.31\end{minipage}32\end{table}33