SPARKNLP-738 Enforcing accuracy to 0 and 1 in classifiers

[danilojsl]

Description

Setting upper and lower bounds when computing accuracy

Motivation and Context

Due to anomalies in the datasets, the actual accuracy can go above 1. This confuses users and makes it look like the model results are unreliable.

How Has This Been Tested?

Screenshots (if appropriate):

• Local tests

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• Code improvements with no or little impact
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

• My code follows the code style of this project.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.
• I have read the CONTRIBUTING page.
• I have added tests to cover my changes.
• All new and existing tests passed.