added usage principles with sklearn-based models

README.md

@@ -120,7 +120,32 @@ it can be either rounded by the researchers to full years, or converted from mon
 
 ## Usage Guidelines
 
-<...>
+Suppose you trained brand-new epigenetic aging clocks model using classic `scikit-learn` library. You saved your model as `pickle` file. Then, the following block of code can be used for benchmarking your model. We also added several other published aging clocks for comparison with yours.
+```python
+from computage import run_benchmark
+
+#first define NaN imputation method for in_library models
+#for simlicity here we recommend to use imputation with zeros
+imputation = 'none'
+models_config = {
+    "in_library":{
+        'HorvathV1':{'imputation':imputation},
+        'Hannum':{'imputation':imputation},
+        'PhenoAgeV2':{'imputation':imputation},
+				},
+	#here we should define a name of our new model as well as path
+    #to the pickle file (.pkl) of the model
+    "new_models":{
+        #'my_new_model_name': {'path':/path/to/model.pkl}
+        }
+}
+#now run the benchmark
+bench = run_benchmark(models_config, 
+        experiment_prefix='my_model_test',
+        output_folder='./benchmark'
+        )
+#upon completion, the results will be saved in the folder you specified
+```
 
 ## Additional Information