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Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.
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With two different random seeds for binary classification, I have the exact same result for ...
With a binary classification analysis, I find myself with the exact same accuracy, recall and specificity for both a qda and glm model.
When I apply the same random seed to both classification model …
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0
answers
604
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Models have high accuracy but very low AUC PR curve
I'm doing balanced (70%-30%) binary classification (yes/no)
I'm trying to combine caret train objects with PRROC's pr.curve function. I'm using a confusion matrix to determine which class is labelled …
2
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0
answers
557
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How to calculate variable importance for different models? is varImp() the solution?
I'm using caret's train() function for a binary classification outcome with different models (nb, knn, lda, qda, glm, rpart, rf).
I'm using varImp() and plots to determine the importance of every fea …
1
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1
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Importance value (with varImp from carret package) for one of the two numerical predictors h...
I'm using two numerical predictors to find an outcome, when using varImp (from the carret package) one of the predictors has 100 importance and the other 0.
How should I interpret this?