# high root mean square error in regression model

I am applying regression to a data of 110 rows and 7 columns ,each having targets. When I applied Lasso to the data and calculated the RMSE value ,the RMSE value is coming to be 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.

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

• Apply grid search for your alpha value and try adding more features and make sure that the problem is solvable by a linear model as it will die lower your alpha value, higher the model will be punishing incorrect classification (let say) – Aditya Mar 20 '18 at 7:59