# METplus Practical Session Guide (Version 5.0) | Binary Categorical Forecasts > METplus solutions for Binary Categorical Forecast Verification

## METplus solutions for Binary Categorical Forecast Verification

Now that you know a bit more about dichotomous, deterministic forecasts and how to extract information on the scalar attributes through statistics, it’s time to show how you can access those same statistics in METplus!

In order to better understand the delineation between METplus, MET, and METplus wrappers which are used frequently throughout this tutorial but are NOT interchangeable, the following definitions are provided for clarity:

• METplus is best visualized as an overarching framework with individual components. It encapsulates all of the repositories: MET, METplus wrappers, METdataio, METcalcpy, and METplotpy.
• MET serves as the core statistical component that ingests the provided fields and commands to compute user-requested statistics and diagnostics.
• METplus wrappers is a suite of Python wrappers that provide low-level automation of MET tools and plotting capability. While there are examples of calling METplus wrappers without any underlying MET usage, these are the exception rather than the rule.

### MET solutions

The MET User’s Guide provides an Appendix that dives into all of the  statistical measure that MET calculates. METplus groups statistics together by application and type and makes them available to METplus users via several line types. For example, many of the statistics that were discussed above can be found in the Contingency Table Statistics (CTS) line type, which logically groups together statistics based directly on contingency table counts. In fact, MET allows users to directly access the contingency table counts through the aptly named Contingency Table Counts (CTC) line type.

The line types that are output by MET depend on your selection of the appropriate line type using the output_flag dictionary. Note that certain line types may or may not be available in every tool: for example, both Point-Stat and Grid-Stat produce CTS line types, which allow users to access the various contingency table statistics for both point-based observations and gridded observations. In contrast, Ensemble-Stat is the only tool that can generate a Ranked Probability Score (RPS) line type, which provides statistics relevant to the analysis of ensemble forecasts. If you don’t see your desired statistic in the line type or tool you’d expect it to be in, be sure to check the Appendix to see if the statistic is available in MET and which line type it’s currently grouped with.

As for the categorical statistics that were just discussed, here’s a link to the User’s Guide Appendix entry that discusses their use in MET:

Remember that for categorical statistics, including those that are associated with probabilistic datasets, you will need to provide an appropriate threshold that divides the observations and forecasts into two mutually exclusive categories. For more information on the available thresholding options, please review this section of the MET User’s Guide.

### METplus Wrapper Solutions

The same statistics that are available in MET are also available with the METplus wrappers. To better understand how MET configuration options for the selection of statistics translate to METplus wrapper configuration options, you can utilize the Statistics and Diagnostics Section of the METplus wrappers User’s Guide, which lists all of the available statistics through the wrappers, including which tools can output particular statistics. To access the line types through the tool, select your desired tool and use this page to view a list of all available commands for that tool. Once you do, you’ll see that the tool will include several options that contain _OUTPUT_FLAG_. These options will exhibit the same behavior and accept the same settings as the line types in MET’s output_flag dictionary, so be sure to review the available settings to get the line type output you want.