Hi There,
So I am working with the fastai’s TabularPandas
.
Following the usual steps:
procs_nn = [Categorify, Normalize]
to_nn = TabularPandas(dataf, procs_nn, cat_names=cat, cont_names=cont,
y_names='turnover_log', splits = splits,
y_block=RegressionBlock(n_out = 1))
learner = tabular_learner(dls, y_range=(-0.96, 3.1), layers=[500, 250],
n_out=1,
loss_func=F.mse_loss,
metrics = [exp_rmspe, rmse])
learner.fit_one_cycle(14, 0.00083)
learner.predict(df_train.iloc[500])
It seems to me like the predictions are in log scale as well as normalize. Does Fastai’s learner object unnormalize and unlogs (exp) the predictions for us or is this something to do “by-hand”
Many thanks