Get Model Summary

Description:

This API will return model Summary. 

API Endpoint: 

/ws/predictive

Method: 

POST

Header: 

Request:

Note: To get the token please refer the article for getToken

Request Example: 

https://<SMARTENURL>/smarten/API/predictive/getModelSummary?tokenid=token

Response:  

A HTML Code is retured as a response which contains below model summary.

Medical Cost Prediction - Model summary

Regression Statistics

The model is an excellent fit and predictions are accurate.

The model is a good fit, and predictions are reasonably accurate.

The model is not a good fit, and predictions are not accurate.Check linearity and normality assumptions in the data to find the fitness of the model.

Residual indicates the difference between actual value and predicted value of an observation.
In order to make valid inferences from regression analysis, the residuals of the regression should follow a normal distribution.

The model strongly follows normal distribution.

R-squared

A coefficient of determination to represent the proportion of variance in the target variable explained by a regression model.

Adjusted R-Squared

A statistical measure determining the variance in the target variable by considering only the predictors influencing the target, rather than considering all chosen predictor variables.

Adjusted R-squared < 0.5: The model is not a good fit, and predictions are not accurate. Check the linearity between each predictor and target variable, and remove redundant variables.

0.5 <= Adjusted R-squared < 0.7: The model is a good fit, and predictions are reasonably accurate.

Adjusted R-squared >= 0.7: The model is a very good fit, and predictions are accurate.

Root Mean Square Error (RMSE)

Square root of the average of squared differences between prediction and actual observation. It is a standard deviation of residual error.

Mean Absolute Error (MAE)

Average of the absolute differences between prediction and actual observation.

Mean Absolute Percentage Error (MAPE)

Mean Absolute Percentage ratio of residual over actual observations.

Mean Percentage Error (MPE)

Mean Percentage Error conveys if there is more positive errors than negative errors or vice-versa based upon its sign.

RMSE, MAE, MAPE and MPE are used to identify the variation in terms of errors from predicted to actual values.

Lower Values (near to zero) of RMSE, MAE, MAPE and MPE represent a better fit of the regression model.

Influencer to analyze: BMI

Postman Example: 

You can import the example below into Postman. 

curl --location --request POST 'https://<SMARTENURL>/smarten/API/predictive/getModelSummary?tokenid=tokenId \

--header 'Cookie: JSESSIONID=SESSIONID=ERhZJyvYmAaV-4esZGgLr0V5f6WWGe8SlKcJHwJs.ip-172-31-8-13'