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Fix english, generalize writing, improve clarity

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robcaulk 2022-07-17 20:58:26 +02:00
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docs/freqai.md
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@ -408,17 +408,9 @@ It is common to want constant retraining, in whichcase, user should set `live_re
### Controlling the model learning process ### 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` Model training parameters are unqiue to the library employed by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries.
models are `n_estimators`, `task_type` and others. For the model like SVM regression model, the accepted parameters are different.
Here we explan the parameters of `model_training_parameters` for `Catboost`: Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. Meanwhile, FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly
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
computational effort and the fit to the training data. If a user has a GPU
installed in their system, they may benefit from changing `task_type` to `GPU`.
The `weight_factor` allows the user to weight more recent data more strongly
than past data via an exponential function: than past data via an exponential function:
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$
@ -427,11 +419,11 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._
![weight-factor](assets/weights_factor.png) ![weight-factor](assets/weights_factor.png)
Finally, `period` defines the offset used for the `labels`. In the present example, `train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally autocorrelated data.
Finally, `label_period_candles` defines the offset used for the `labels`. In the present example,
the user is asking for `labels` that are 24 candles in the future. the user is asking for `labels` that are 24 candles in the future.
Note: typically in time-series forecasting, the validation/test data should be the "future" by a given training data. Thus, it is recommended to disable `shuffle` parameter during the cross-validation or validation steps. For more detailed explaination, visit [here](https://medium.com/@soumyachess1496/cross-validation-in-time-series-566ae4981ce4).
### Removing outliers with the Dissimilarity Index ### Removing outliers with the Dissimilarity Index
The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each