Hey there fellow learners!

Just a quick question here.

Screen Link:

https://app.dataquest.io/m/154/cross-validation/2/holdout-validation

My Code:

```
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error
train_one = split_one
test_one = split_two
train_two = split_two
test_two = split_one
knn = KNeighborsRegressor()
knn.fit(train_one[['accommodates']],train_one['price'])
prediction_one = knn.predict(test_one[['accommodates']])
msq_one = mean_squared_error(test_one['price'],prediction_one)
iteration_one_rmse = msq_one**0.5
knn_two = KNeighborsRegressor()
knn_two.fit(train_two[['accommodates']],train_two['price'])
prediction_two = knn_two.predict(test_two[['accommodates']])
msq_two = mean_squared_error(test_two['price'],prediction_two)
iteration_two_rmse = msq_two**0.5
avg_rmse = np.mean([iteration_two_rmse,iteration_one_rmse])
```

What I expected to happen:

```
avg_rmse = 128.96254732948216
```

What actually happened:

```
avg_rmse = 123.7207888486061
```

So I seem to be slightly off here. I checked it with the answer and what happened is that I should not have started knn_two. This seems to be a bit counter intuitive to me, because the original knn already has the test_two values in it right? So it would unfairly improve its learning capabilities. Therefore I started a new knn.

I am a bit confused why I should not start another knn.

Cheers!