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Sub - sample these datato createunbalanced dataset
We can resample our training datasetto createbalanced dataset
I have created a synthetic dataset , with 20 samples in one class and 100 in the otherthus creatingan imbalanced dataset
instances in the minority classresultin the imbalanced dataset
Less than 1 % of the observations are positivecreatingan imbalanced dataset
that since we depend on input data from different users , the number of documents in our training set from different classes could vary widelycreatingan imbalanced dataset
From this post , I knowcan setscale_pos_weight for an imbalanced dataset
% 60-%40 , % 60-%20-%20 , % 50-%15-%15-%20can also leadimbalanced dataset problem
To illustrate DummyClassifier , first letcreatean imbalanced dataset
to label most of the examples as the majority class for the UPDRSresultedin imbalanced dataset
to inaccurate results even when brilliant models are used to process that datacan leadto inaccurate results even when brilliant models are used to process that data
problems in the detection of under - represented Action Unitscauseproblems in the detection of under - represented Action Units
a lot of frustrationcan causea lot of frustration
to problem in classification accuracycould leadto problem in classification accuracy
deflective classification resultsmay causedeflective classification results
to underfitting of classifiers in the final ensembleleadsto underfitting of classifiers in the final ensemble
a minimum number of times a word that does not appear in the target categorysetsa minimum number of times a word that does not appear in the target category
to overrated accuracy resultsleadingto overrated accuracy results
to inflated performance estimatesmay leadto inflated performance estimates
a skewed classification of the predicting targetwill causea skewed classification of the predicting target
the error(passive) caused bythe error
seriously negative effects on classification accuracycan causeseriously negative effects on classification accuracy
likely(passive) to be caused bylikely
a seriously negative effect on classification accuracy [ 35can causea seriously negative effect on classification accuracy [ 35
the classification result(passive) caused bythe classification result
bad resultsmay causebad results
Now , I was wondering(passive) caused byNow , I was wondering