From 3fefa4b144243c3534c42deace3228571647cef3 Mon Sep 17 00:00:00 2001 From: longyu Date: Mon, 11 Jul 2022 23:36:01 +0200 Subject: [PATCH] add shuffle parameter explaination --- docs/freqai.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/freqai.md b/docs/freqai.md index a0a11ac35..a1b00c75c 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -408,6 +408,11 @@ It is common to want constant retraining, in whichcase, user should set `live_re ### Controlling the model learning process +Depending on what AI model to be used, these parameter names could be different. For example, the accepted parameters for the `Catboost` +models are `data_split_parameters`, `n_estimators` and etc. For the model like SVM regression model, the accepted parameters are different. + +Here we explan the parameters of `model_training_parameters` for `Catboost`: + The user can define model settings for the data split `data_split_parameters` and learning parameters `model_training_parameters`. Users are encouraged to visit the Catboost documentation for more information on how to select these values. `n_estimators` increases the @@ -425,6 +430,8 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._ Finally, `period` defines the offset used for the `labels`. In the present example, the user is asking for `labels` that are 24 candles in the future. +Note: since we work time series data and want to train a AI model to predict the future, the validation/test data should be the "future" by a given training data. Thus, we strongly recommend to disable `shuffle` parameter during the cross-validation steps. For more detailed explaination, visit [here](https://medium.com/@soumyachess1496/cross-validation-in-time-series-566ae4981ce4). + ### Removing outliers with the Dissimilarity Index The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each