Use python predictit/main.py --help for more parameters' info. Example of compare_models function. You can compare it on same data in various parts or on predictit different data (check configuration on how to insert dictionary with data names) To optimize hyperparameters, just set optimizeit: 1, and model parameters limits. It is commented in it's docstrings how to use it. It's not grid bruteforce. It is a heuristic method based on halving interval is used, but still it predictit can be time-consuming. It is recommended only to tune parameters worth of it. Or tune it by parts. C ve Siz, predictit Fatih Ekici.
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You can use subcategories like general , data_input , output. You can also use config instance directly for all the attributes and omit the subcategories (though without intellisense help). Multiple parameters at once with dictionary and update function. If you need create more configurations and don't want to override its values, you can create multiple instances, but you need to insert this new config as function parameter. Although there are many config variables, defaults should be enough. There are only two positional arguments data and predicted_column (because, there is more than a hundred configurable values). So you can use also. Lose ile ilgili cümleler.
There are also two libraries - Tensorflow and pyodbc not in requirements, because not necessary, but troublesome. If library not installed with pip, check which library don't work, install manually with stackoverflow and repeat. There are some libraries that not every user will be using (e.g. Tensorflow or libraries for some data inputs). If you want to be sure to have all libraries, you can download requirements_advanced.txt and then install advanced requirements with pip install -r requirements_advanced.txt . Library was developed during 2020 and structure and even API (configuration) changed a lot. From version 2.0 it's considered to be stable and following semantic versioning.
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