What is 'f1 score' in SAP CA-ML-DAR - ?

f1 score - Overview

• Component: CA-ML-DAR

• Component Name:

• Description: Data Attribute Recommendation Classification metric. Harmonic mean of precision and recall. The higher the better.

f1 score - Details

• 
  Key Concepts: 
  F1 score is a metric used to measure the accuracy of a model in a classification problem. It is the harmonic mean of precision and recall, and is calculated by taking the average of true positives and true negatives divided by the sum of false positives and false negatives. The higher the F1 score, the better the model is at predicting correctly. 
  
  How to use it: 
  The F1 score can be used to evaluate the performance of a model in a classification problem. It is calculated by taking the average of true positives and true negatives divided by the sum of false positives and false negatives. The higher the F1 score, the better the model is at predicting correctly. 
  
  Tips & Tricks: 
  When using F1 score to evaluate a model, it is important to consider other metrics such as precision and recall as well. Additionally, it is important to consider the context in which the model is being used, as different contexts may require different metrics for evaluation. 
  
  Related Information: 
  The F1 score is part of SAP's Component CA-ML-DAR (Data Analysis & Reporting). This component provides tools for data analysis and reporting, including predictive analytics, machine learning, and natural language processing. It also provides tools for data visualization, such as charts and graphs.