Hi,

I am running into an error while running **09_tabular** with the latest version of the code and fastai 2.1.7 and encountered an error in the **6th code cell** of the **Using a Neural Network** part. Here is the cell:

xs_filt2 = xs_filt.drop(‘fiModelDescriptor’, axis=1)

valid_xs_time2 = valid_xs_time.drop(‘fiModelDescriptor’, axis=1)

m2 = rf(xs_filt2, y_filt)

m_rmse(m, xs_filt2, y_filt), m_rmse(m2, valid_xs_time2, valid_y)

And here is the error:

ValueError Traceback (most recent call last)

in

2 valid_xs_time2 = valid_xs_time.drop(‘fiModelDescriptor’, axis=1)

3 m2 = rf(xs_filt2, y_filt)

----> 4 m_rmse(m, xs_filt2, y_filt), m_rmse(m2, valid_xs_time2, valid_y)in m_rmse(m, xs, y)

1 def r_mse(pred,y): return round(math.sqrt(((pred-y)**2).mean()), 6) #jdm: the last arg here (6) is the number of decimal points

----> 2 def m_rmse(m, xs, y): return r_mse(m.predict(xs), y)/opt/conda/envs/fastai/lib/python3.8/site-packages/sklearn/ensemble/_forest.py in predict(self, X)

781 check_is_fitted(self)

782 # Check data

–> 783 X = self._validate_X_predict(X)

784

785 # Assign chunk of trees to jobs/opt/conda/envs/fastai/lib/python3.8/site-packages/sklearn/ensemble/_forest.py in

validate_X_predict(self, X)[0]._validate_X_predict(X, check_input=True)

419 check_is_fitted(self)

420

–> 421 return self.estimators

422

423 @property/opt/conda/envs/fastai/lib/python3.8/site-packages/sklearn/tree/_classes.py in

validate_X_predict(self, X, check_input)!= n_features:

394 n_features = X.shape[1]

395 if self.n_features

–> 396 raise ValueError("Number of features of the model must "

397 "match the input. Model n_features is %s and "

398 "input n_features is %s "ValueError: Number of features of the model must match the input. Model n_features is 15 and input n_features is 14

Any ideas?

PS: for the time being I just ignored it since it does not impact the rest of the program…

Thanks,