View RSS Feed

newdigital

Cross-validation and basics of causal inference in CatBoost models, export to ONNX format

Rate this Entry
by , 05-19-2024 at 02:27 PM (81 Views)
      
   
In the previous articles, I have described various ways to use machine learning algorithms to create trading systems. Some turned out to be quite successful, others (mostly from early publications) were greatly overtrained. Thus, the sequence of my articles reflects the evolution of understanding: what machine learning is actually capable of. We are, of course, talking about the classification of time series.

The current article is a development of the previous topic and the next step towards creating a self-training algorithm that is able to look for patterns in data while minimizing overfitting. After all, we want to get a real effect from the use of machine learning, so that it not only generalizes training examples, but also determines the presence of cause-and-effect relationships in them.
more...

Submit "Cross-validation and basics of causal inference in CatBoost models, export to ONNX format" to Google Submit "Cross-validation and basics of causal inference in CatBoost models, export to ONNX format" to del.icio.us Submit "Cross-validation and basics of causal inference in CatBoost models, export to ONNX format" to Digg Submit "Cross-validation and basics of causal inference in CatBoost models, export to ONNX format" to reddit

Tags: None Add / Edit Tags
Categories
Uncategorized

Comments