It shows you the evolution of the batterys capacity during the charge discharge cycles and computes a few important battery parameters, such as the wear level and discharge cycles count.This program continuously reads the battery data, making a prediction for the time remaining; it also features two alarms, for low critical battery capacity, triggered by either values of time remaining or capacity percentage you set.Both Calibration and Fast Discharge procedures were updated to perform better on Windows laptops or tablets.
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So in order to get the top 20 features youll want to sort the features from most to least important for instance like this: importances forest.featureimportances indices numpy.argsort(importances)-20: (-20: because.The dataset is small (about 160 examples) and unbalanced i.e.I have classes with few examples.So far I further limited the dataset to 110 examples in order to work with a balanced training set.
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