Item: DataRobot
Rating: 8
Author: Bennalyn Sta. Ana

Overall Satisfaction with DataRobot

Use Cases and Deployment Scope

I am using datarobot to develop Application and Behavioural Credit Scorecards for the Bank. Develop credit risk models to be used for various business operations (i.e., Products, Credit, and Collections), such as cross-selling, credit limit selling, and collection strategy formulation. Develop credit risk models to elevate lending decision-making and enhance risk management at CIMB PH.

Pros and Cons

Pros

Exploratory Data Analysis
Shortlisting of Risk Factors
Model Building/ Blueprint

Cons

Show the model performance of train dataset
Do not limit up to five features only when downloading predictions

Return on Investment

less time spent on building models
increased productivity
there are some limitations to consider when using datarobot like interpretability of model results

Key Insights

Do you think DataRobot delivers good value for the price?

Yes

Are you happy with DataRobot's feature set?

Yes

Did DataRobot live up to sales and marketing promises?

Yes

Did implementation of DataRobot go as expected?

I wasn't involved with the implementation phase

Would you buy DataRobot again?

Yes

Other Software Used

Python IDLE

Likelihood to Recommend

Predictive Modeling. Using Datarobot, I was able to build accurate predictive models quickly. It is also very useful in shortlisting risk factors, it provides Feature associations to include only the most relevant features in final model to reduce complexity and improve interpretability.

Comments

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Risk Modeller's Assessment of DataRobot