Very good demonstration, thank you!
You're welcome!
could you please share the dataset?
So is the model a generalized model? since the MSE is way too high. Or we have to optimize it better by performing Hyperparameter tuning? Please reply
Yes ur right if MSE values are high try to optimize it using hyperparameter tuning or try different algorithms and experiment it :)
Yes please try different hyperparameters to optimize your model.
The sklearn library doesnt accept "mse" as a valid value for the criterion parameter in the DecisionTreeRegressor instead use "squared_error" (This uses the mean squared error (MSE) as the criterion for splitting nodes )
I have not checked newer version of sklearn library.
Very good demonstration, thank you!
You're welcome!
could you please share the dataset?
So is the model a generalized model? since the MSE is way too high. Or we have to optimize it better by performing Hyperparameter tuning? Please reply
Yes ur right
if MSE values are high try to optimize it using hyperparameter tuning or try different algorithms and experiment it :)
Yes please try different hyperparameters to optimize your model.
The sklearn library doesnt accept "mse" as a valid value for the criterion parameter in the DecisionTreeRegressor instead use "squared_error" (This uses the mean squared error (MSE) as the criterion for splitting nodes )
I have not checked newer version of sklearn library.