Increased network buffer size to 33792 bytes.

Signed-off-by: Toni Uhlig <matzeton@googlemail.com>

1 files changed, 2 insertions, 5 deletions

diff --git a/examples/README.md b/examples/README.md
index 71b7b8204..eb00539d9 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -37,15 +37,12 @@ Prints prettyfied information about flow events.
 
 Use sklearn together with CSVs created with **c-analysed** to train and predict DPI detections.
 
-Try it with: `./examples/py-machine-learning/sklearn-ml.py --csv ./ndpi-analysed.csv --proto-class tls.youtube --proto-class tls.github --proto-class tls.spotify --proto-class tls.facebook --proto-class tls.instagram --proto-class tls.doh_dot --proto-class quic --proto-class icmp`
+Try it with: `./examples/py-machine-learning/sklearn_random_forest.py --csv ./ndpi-analysed.csv --proto-class tls.youtube --proto-class tls.github --proto-class tls.spotify --proto-class tls.facebook --proto-class tls.instagram --proto-class tls.doh_dot --proto-class quic --proto-class icmp`
 
 This way you should get 9 different classification classes.
 You may notice that some classes e.g. TLS protocol classifications may have a higher false-negative rate.
-
 Unfortunately, I can not provide any datasets due to some privacy concerns.
-But you can use a [pre-trained model](https://drive.google.com/file/d/1KEwbP-Gx7KJr54wNoa63I56VI4USCAPL/view?usp=sharing) with `--load-model` using python-joblib.
-Please send me your CSV files to improve the model. I will treat those files confidential.
-They'll only be used for the training process and purged afterwards.
+But you can use a [pre-trained model](https://drive.google.com/file/d/1KEwbP-Gx7KJr54wNoa63I56VI4USCAPL/view?usp=sharing) with `--load-model` and the aformentioned parameters.
 
 ## py-flow-dashboard