How to reduce RMSE/SE when it's too high
Hi All,
My data has 2 integers and all other polynomial attributes
id
state
year
month
leads (int)
responses (int)
typeOfMail
status
I used split model where I split my data between 20 and 2 months for 22 months and I got 12.41 RSME and squared_error: 154.176 +/- 335.663.
I don't know how to reduce this and also not sure if I can apply any other models because I believe my options are limited
I already tried using other combinations in applying these models like adding K-NN and decision tree but that didn't help
Also, tried to split data between 18 and 4 months for total of 22 which didn't help either
what should I do?
My data has 2 integers and all other polynomial attributes
id
state
year
month
leads (int)
responses (int)
typeOfMail
status
I used split model where I split my data between 20 and 2 months for 22 months and I got 12.41 RSME and squared_error: 154.176 +/- 335.663.
I don't know how to reduce this and also not sure if I can apply any other models because I believe my options are limited
I already tried using other combinations in applying these models like adding K-NN and decision tree but that didn't help
Also, tried to split data between 18 and 4 months for total of 22 which didn't help either
what should I do?
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Hi @varunm1
Thank you for your response. Where do I add "Automatic feature engineering" operator in my process as you can see the screenshots from above.
Should I use both the operators together and by saying " should be on the training side of the validation operator" you mean at the same place where I have my models so one more link will go from my "multiply" operator to one of these and then they go to the model?
I would like to use automatic feature generation but not sure how and where should I place that..... I am using last 2 years of data which I still think is not enough for this type of prediction.
kindly help me with the next steps thank you.
Thank you for your response. Where do I add "Automatic feature engineering" operator in my process as you can see the screenshots from above.
Should I use both the operators together and by saying " should be on the training side of the validation operator" you mean at the same place where I have my models so one more link will go from my "multiply" operator to one of these and then they go to the model?
I would like to use automatic feature generation but not sure how and where should I place that..... I am using last 2 years of data which I still think is not enough for this type of prediction.
kindly help me with the next steps thank you.
Here is a sample process I built quickly. You can see inside the validation operator and also observe the parameters I selected for each operator.
You can import this by downloading into your PC and selecting File --> import process in rapidminer.
You can import this by downloading into your PC and selecting File --> import process in rapidminer.
Thank you I am trying to run it this way right now.
@varunm1
Thank you for your responses.
I got this error. I am still a little confused as to which parameters I should choose inside "Optimize Parameter (grid)" operator.
I did chose what was there in the sample but I got the below error so now I am going to run it again with something else.
For some reason each run takes about 25 mins to complete so not sure how to reduce that but getting lower error rate is important
Thank you for your responses.
I got this error. I am still a little confused as to which parameters I should choose inside "Optimize Parameter (grid)" operator.
I did chose what was there in the sample but I got the below error so now I am going to run it again with something else.
For some reason each run takes about 25 mins to complete so not sure how to reduce that but getting lower error rate is important
Hello @User111113
The optimize hyperparameter will test different settings of the algorithm. It will try all the combinations of hyperparameters and select best based on low error rate.
It takes time as the algorithms is trying multiple combinations. For example hyperparamters in a decision tree are the ones you can find in parameters, you can see how many combinations will be trained. Try reading help of optimize parameter grid operator and also hyperparamter tuning on google.
I just gave a sample, you need to change based on your understanding and requirement.
The optimize hyperparameter will test different settings of the algorithm. It will try all the combinations of hyperparameters and select best based on low error rate.
It takes time as the algorithms is trying multiple combinations. For example hyperparamters in a decision tree are the ones you can find in parameters, you can see how many combinations will be trained. Try reading help of optimize parameter grid operator and also hyperparamter tuning on google.
I just gave a sample, you need to change based on your understanding and requirement.
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Hello @User111113
The optimize hyperparameter will test different settings of the algorithm. It will try all the combinations of hyperparameters and select best based on low error rate.
It takes time as the algorithms is trying multiple combinations. For example hyperparamters in a decision tree are the ones you can find in parameters, you can see how many combinations will be trained. Try reading help of optimize parameter grid operator and also hyperparamter tuning on google.
I just gave a sample, you need to change based on your understanding and requirement.
The optimize hyperparameter will test different settings of the algorithm. It will try all the combinations of hyperparameters and select best based on low error rate.
It takes time as the algorithms is trying multiple combinations. For example hyperparamters in a decision tree are the ones you can find in parameters, you can see how many combinations will be trained. Try reading help of optimize parameter grid operator and also hyperparamter tuning on google.
I just gave a sample, you need to change based on your understanding and requirement.
You can add feature selection and hyperparameter optimization into your process. Feature selection can be done using "Automatic feature engineering" operator, and hyperparameter selection is done using "Optimize Hyperprarameter (Grid)". Both of these should be on the training side of the validation operator.
I am not sure how much data you have, but if it is not a significant amount, then your models might overfit as you are using two complex and data-hungry algorithms.
You can also generate new features using the same automatic feature engineering.