From 3fc79dd8d45bbf0e992dc7859ef4cb445669f0ef Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 6 Jan 2025 11:41:15 +0000 Subject: [PATCH] Deployed 82e3e3d to develop in en with MkDocs 1.6.1 and mike 2.1.3 --- en/develop/freqai/index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/en/develop/freqai/index.html b/en/develop/freqai/index.html index 736204c65..cdd894132 100644 --- a/en/develop/freqai/index.html +++ b/en/develop/freqai/index.html @@ -1810,7 +1810,7 @@
  • Extensibility - The generalized and robust architecture allows for incorporating any machine learning library/method available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network
  • Smart outlier removal - Remove outliers from training and prediction data sets using a variety of outlier detection techniques
  • Crash resilience - Store trained models to disk to make reloading from a crash fast and easy, and purge obsolete files for sustained dry/live runs
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  • Automatic data normalization - Normalize the data in a smart and statistically safe way
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  • Automatic data normalization - Normalize the data in a smart and statistically safe way
  • Automatic data download - Compute timeranges for data downloads and update historic data (in live deployments)
  • Cleaning of incoming data - Handle NaNs safely before training and model inferencing
  • Dimensionality reduction - Reduce the size of the training data via Principal Component Analysis