Practice Interview Questions
| Question | Status |
|---|---|
Creating a naïve forecast entails setting all forecasts to the last observation's value. In our scenario, we apply this to the "distance per dollar" metric (calculated as distance_to_travel/monetary_cost). Summing distance and cost on a monthly basis yields actual values, and forecasted values come from the previous month. To assess accuracy, we use the root mean squared error (RMSE), computed as sqrt(mean(square(actual - forecast))). Report the rounded RMSE to the 2nd decimal place for a concise analysis.
uber_request_logs table:
| Field | Description |
|---|---|
| request_id | Identifier of request |
| request_date | Date of request |
| request_status | Status of request |
| distance_to_travel | Distance traveled |
| monetary_cost |
| Cost of travel |
| driver_to_client_distance | Distance between users |
Example output:
| rmse |
|---|
| 2.34 |
Creating a naïve forecast entails setting all forecasts to the last observation's value. In our scenario, we apply this to the "distance per dollar" metric (calculated as distance_to_travel/monetary_cost). Summing distance and cost on a monthly basis yields actual values, and forecasted values come from the previous month. To assess accuracy, we use the root mean squared error (RMSE), computed as sqrt(mean(square(actual - forecast))). Report the rounded RMSE to the 2nd decimal place for a concise analysis.
uber_request_logs table:
| Field | Description |
|---|---|
| request_id | Identifier of request |
| request_date | Date of request |
| request_status | Status of request |
| distance_to_travel | Distance traveled |
| monetary_cost | Cost of travel |
| driver_to_client_distance | Distance between users |
Example output:
| rmse |
|---|
| 2.34 |