Open Source Python Software Testing Tools

The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming"

Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran.

Here we present the lines of code for creating a taylor plot with python to display several machine learning models. We show the solution for displaying 10 models, but the list and number can be changed simply by modifying the sample list.