doc update

docs/en_US/hpo_benchmark.rst

@@ -4,18 +4,19 @@ HPO Benchmarks
 ..  toctree::
     :hidden:
 
-    HPO Benchmark Example Statistics <hpo_benchmark_statistics>
+    HPO Benchmark Example Statistics <hpo_benchmark_stats>
 
-We provide a benchmarking tool to compare the performances of tuners provided by NNI (and users' custom tuners) on different tasks. The implementation of this tool is based on the automlbenchmark repository (https://github.com/openml/automlbenchmark), which provides services of running different *frameworks* against different *benchmarks* consisting of multiple *tasks*. The tool is located in ``examples/trials/benchmarking/automlbenchmark``. This document provides a brief introduction to the tool and its usage. 
+We provide a benchmarking tool to compare the performances of tuners provided by NNI (and users' custom tuners) on different
+types of tasks. This tool uses the `automlbenchmark repository <https://github.com/openml/automlbenchmark)>`_  to run different *benchmarks* on the NNI *tuners*.
+The tool is located in ``examples/trials/benchmarking/automlbenchmark``. This document provides a brief introduction to the tool and its usage.
 
 Terminology
 ^^^^^^^^^^^
 
-
 * **task**\ : a task can be thought of as (dataset, evaluator). It gives out a dataset containing (train, valid, test), and based on the received predictions, the evaluator evaluates a given metric (e.g., mse for regression, f1 for classification). 
 * **benchmark**\ : a benchmark is a set of tasks, along with other external constraints such as time and resource. 
 * **framework**\ : given a task, a framework conceives answers to the proposed regression or classification problem and produces predictions. Note that the automlbenchmark framework does not pose any restrictions on the hypothesis space of a framework. In our implementation in this folder, each framework is a tuple (tuner, architecture), where architecture provides the hypothesis space (and search space for tuner), and tuner determines the strategy of hyperparameter optimization. 
-* **tuner**\ : a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user. 
+* **tuner**\ : a `tuner or advisor <https://nni.readthedocs.io/en/stable/builtin_tuner.html>`_ provided by NNI, or a custom tuner provided by the user.
 * **architecture**\ : an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See ``./nni/extensions/NNI/architectures`` for examples.
 
 Note: currently, the only architecture supported is random forest. The architecture implementation and search space definition can be found in ``./nni/extensions/NNI/architectures/run_random_forest.py``. The tasks in benchmarks "nnivalid" and "nnismall" are suitable to solve with random forests.