EuroTruckSimulator2RoadtotheBlackSeaAtivadordownload LINK 🎇

EuroTruckSimulator2RoadtotheBlackSeaAtivadordownload LINK 🎇



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What is the relationship between fit and better_fit in scikit-learn

I’m new to the world of Machine Learning (mainly scikit-learn). I’ve been trying to use decision trees in one of my projects. Based on the scikit-learn documentation, I understand that “fit” can be used to build a tree. However, to “make the best model” is used “better_fit”.
Could someone explain what this means and show some code?


The function better_fit is used to find the best model from a list of models.
With the default values for the other parameters it just returns the first model from the model list. So fitting is the default behaviour.
In many real world use-cases there is no need to have a better_model (it’s usually the same as fit) and one might actually only use a model if the other parameters are set to something other than default.
If you want to use the “best model” the docs (first line) should be your first port of call.

Some “best” model examples (random forest):
>>> from sklearn import tree
>>> from sklearn.datasets import load_iris
>>> ir

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