You can download an approximation of the model in python with DataRobot Prime. You can also down the exact version in java in the downloads tab. Both of these features are available under the predict tab of the model that you want to download.
This feature is so that you can run the model while not connected to a network, or at very low latency.
Thank you for your colaboration, I would like to ask you where could I find fast & accurate algorithms into the DataRobot plateform. I am trying DataRobot for 14 days, I have only found "RECOMMENDED FOR DEPLOYMENT" .
Another thing, why I couldn't find DataRobot Prime model
Enclose the picture:
Could you help me please to get FAST & ACCURATE and DataRobot Prime
Thank you very much for your time
Hi Dr. Youness,
I believe we don't use the "Fast & Accurate" tag anymore. Instead we just have the "recommended for deployment." This refers to the most accurate non-blender model (because blenders can take longer to run). This is likely going to give you a model that is fast and accurate.
We do not offer DataRobot Prime for the free trial.
We have a flexible pricing strategy that allows you to add functionality as you progress with the platform. I will introduce you to someone who can get you more details via email. That way you can get all of your questions about pricing answered
I would like to ask you, If datarobot can be used to perform interpretation of experimental data into the form as a report?
If the answer is yes, so how?
Thank you very much for your time
Have a great day!
Thank you very much for you answer. However, I need to know how to perform interpretation of experimental data into a report analysis.
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.