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In k-fold cross validation, the data set is split into k disjoint subsets with different sizes.
In k-fold cross validation, the data set is split into k disjoint subsets with different sizes.
False
The usual value for k in k-fold cross validation is 5.
The usual value for k in k-fold cross validation is 5.
False
In stratified cross validation, each fold has a random distribution of labels.
In stratified cross validation, each fold has a random distribution of labels.
False
In leave one out cross validation, each example is used as the test set and all other examples are used as the training set.
In leave one out cross validation, each example is used as the test set and all other examples are used as the training set.
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2-fold cross validation is a variation of k-fold cross validation with a fixed value of k.
2-fold cross validation is a variation of k-fold cross validation with a fixed value of k.
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The classifier returns a classification result for each example in the test case without a probability estimate.
The classifier returns a classification result for each example in the test case without a probability estimate.
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A lift curve is drawn by counting the actual Positive examples in each bin of the ranked examples.
A lift curve is drawn by counting the actual Positive examples in each bin of the ranked examples.
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The evaluation of classification methods uses a test set with unlabeled examples.
The evaluation of classification methods uses a test set with unlabeled examples.
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The goal of the classification method is to produce a lift curve below the random line in the lift chart.
The goal of the classification method is to produce a lift curve below the random line in the lift chart.
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The classifier method is considered poor if the lift curve is way above the random line in the lift chart.
The classifier method is considered poor if the lift curve is way above the random line in the lift chart.
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