What is 'mean squared error' in SAP CA-ML-DAR - ?

mean squared error - Overview

• Component: CA-ML-DAR

• Component Name:

• Description: Data Attribute Recommendation Regression metric. Average squared difference between the predicted values and the actual values. The lower the better.

mean squared error - Details

• 
  Key Concepts: 
  Mean Squared Error (MSE) is a measure of the difference between the predicted values and the actual values in a dataset. It is used to evaluate the accuracy of a predictive model. MSE is calculated by taking the average of the squared differences between the predicted and actual values. 
  
  How to use it: 
  MSE can be used to compare different predictive models and determine which one is more accurate. It can also be used to identify areas where a model needs improvement. For example, if a model has a high MSE, it may indicate that the model is overfitting or underfitting the data. 
  
  Tips & Tricks: 
  When using MSE, it is important to remember that lower values indicate better accuracy. Additionally, it is important to consider other metrics such as precision and recall when evaluating a model’s performance. 
  
  Related Information: 
  MSE is part of the Component CA-ML-DAR (Data Analysis and Reporting) in SAP. This component provides tools for data analysis, predictive modeling, and reporting. It also includes other metrics such as root mean squared error (RMSE) and mean absolute error (MAE).