IB1000: Imbalanced data with 1000 positives and 10000 negatives.
In precrec: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves
IB1000 R Documentation
Imbalanced data with 1000 positives and 10000 negatives.
Description
A list contains labels and scores of five different performance levels.
All scores were randomly generated.
Usage
data(IB1000)
Format
A list with 8 items.
- np
number of positives: 1000
- nn
number of negatives: 10000
- labels
labels of observed data
- random_scores
scores of a random performance level
- poor_er_scores
scores of a poor early retrieval level
- good_er_scores
scores of a good early retrieval level
- excel_scores
scores of an excellent level
- perf_scores
scores of the perfect level
precrec documentation built on Oct. 12, 2023, 1:06 a.m.
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| IB1000 | R Documentation |
Imbalanced data with 1000 positives and 10000 negatives.
Description
A list contains labels and scores of five different performance levels. All scores were randomly generated.
Usage
data(IB1000)
Format
A list with 8 items.
- np
number of positives: 1000
- nn
number of negatives: 10000
- labels
labels of observed data
- random_scores
scores of a random performance level
- poor_er_scores
scores of a poor early retrieval level
- good_er_scores
scores of a good early retrieval level
- excel_scores
scores of an excellent level
- perf_scores
scores of the perfect level
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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