Code of the model

Code of the model

Is it possible to get the underlying code that generates a specific algorithm run by DataRobot, for example as a Python script? It may be useful to replicate the model in a script and apply some desired changes.

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Hi Doctor Youness, 

Yes, you can use DataRobot on experimental data, including scientific data.  We have customers who do this for various use cases.  


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Thank you very much for you answer. However, I need to know how to perform interpretation of experimental data into a report analysis.

I can see that in DataRobot:
1. Enter Data
2. Prepare Data
3. Set Model Target
4. Start Model Search
5. Explain Models
Could you give me an example, please, showing analysis, reports and interpretation performed by DataRobot?
Thank you very much for time
Best regards
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Sure thing Dr. Youness, 


Here is a detailed walkthrough of how to use AutoML to import data, set the target, build models and explain them.


We also have a demo video and article that covers how to use the platform to make predictions and interpret the results..  


I also recommend watching this tutorial on making predictions about the spread of COVID-19 as another example. 

If you are looking for more formal reports of analyses being done with DataRobot, here is a list of peer-reviewed publications you can read to get an idea on the research potential. 


Tsuzuki, S., Fujitsuka, N., Horiuchi, K., Ijichi, S., Gu, Y., Fujitomo, Y., ... & Ohmagari, N. (2020). Factors associated with sufficient knowledge of antibiotics and antimicrobial resistance in the Japanese general population. Scientific Reports, 10(1), 1-9.

Hatae, R., Chamoto, K., Kim, Y. H., Sonomura, K., Taneishi, K., Kawaguchi, S., ... & **bleep**arasan, S. (2020). Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy. JCI insight, 5(2)

Suzuki S1, Yama**bleep**a T1, Sakama T2, Arita T1, Yagi N1, Otsuka T1, Semba H1, Kano H1, Matsuno S1, Kato Y1, Uejima T1, Oikawa Y1, Matsuhama M3, Yajima J1. (2019) Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis. PLoS One, 14(9)

Muhlestein, W. E., Akagi, D. S., Davies, J. M., & Chambless, L. B. (2018). Predicting inpatient length of stay after brain tumor surgery: Developing machine learning ensembles to improve predictive performance. Neurosurgery.

Cenik, C., Chua, H. N., Singh, G., Akef, A., Snyder, M. P., Palazzo, A. F., ... & Roth, F. P. (2017). A common class of transcripts with 5′-intron depletion, distinct early coding sequence features, and N1-methyladenosine modification. RNA, 23(3), 270-283.

Muhlestein, W. E., Akagi, D. S., Chotai, S., & Chambless, L. B. (2017). The impact of race on discharge disposition and length of hospitalization after craniotomy for brain tumor. World neurosurgery, 104, 24-38.

Muhlestein, W. E., Akagi, D. S., Chotai, S., & Chambless, L. B. (2017). The impact of presurgical comorbidities on discharge disposition and length of hospitalization following craniotomy for brain tumor. Surgical neurology international, 8.


I hope this helps. 



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Thank you so much

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