I am applying regression to a dataset comprising 110 rows and 7 columns each with targets. When I applied Lasso regression to the data and calculated the RMSE value, the RMSE value was 13.11
. I think the RMSE value should be close to zero. What is the permissible values of RMSE in a regression model? What could have gone wrong in the computation?
My code:
from sklearn import linear_model
reg = linear_model.Lasso(alpha = .00001)
reg.fit(Xt,Yt)
ans=reg.predict(Xts)
print(ans)
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Yts, ans))
Whereas when I try cross validation the MSE scores are way below 0.35
kfold = KFold(n_splits=10)
results = cross_val_score(reg, full_data, target, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
results
Results: -0.13 (0.45) MSE