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Added features statistics and raw numbers of class imbalance
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README.md
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README.md
@ -3,7 +3,7 @@ Andrzej Wójtowicz
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Document generation date: 2016-04-16 01:21:55.
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Document generation date: 2016-04-16 13:55:00.
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@ -62,7 +62,16 @@ Document generation date: 2016-04-16 01:21:55.
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```
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**Class imbalance**: 11% / 89%
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**Predictors**:
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|Class | Frequency|
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|:--------------|---------:|
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|factor | 6|
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|integer | 3|
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|numeric | 5|
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|ordered factor | 3|
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**Class imbalance**: 11% / 89% (4254 / 33973)
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---
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@ -121,7 +130,13 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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```
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**Class imbalance**: 37% / 63%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|numeric | 30|
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**Class imbalance**: 37% / 63% (212 / 357)
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---
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@ -158,7 +173,13 @@ O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
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```
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**Class imbalance**: 35% / 65%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|integer | 9|
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**Class imbalance**: 35% / 65% (239 / 444)
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---
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@ -214,7 +235,16 @@ Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of C
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```
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**Class imbalance**: 22% / 78%
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**Predictors**:
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|Class | Frequency|
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|:--------------|---------:|
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|factor | 9|
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|integer | 17|
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|numeric | 2|
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|ordered factor | 1|
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**Class imbalance**: 22% / 78% (471 / 1655)
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---
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@ -264,7 +294,14 @@ Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for
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```
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**Class imbalance**: 22% / 78%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|factor | 3|
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|integer | 20|
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**Class imbalance**: 22% / 78% (6636 / 23364)
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---
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@ -302,7 +339,15 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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```
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**Class imbalance**: 29% / 71%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|factor | 1|
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|integer | 4|
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|numeric | 5|
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**Class imbalance**: 29% / 71% (167 / 416)
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---
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@ -341,7 +386,13 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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```
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**Class imbalance**: 35% / 65%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|numeric | 10|
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**Class imbalance**: 35% / 65% (6688 / 12332)
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---
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@ -383,7 +434,14 @@ Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of d
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```
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**Class imbalance**: 7% / 93%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|factor | 4|
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|integer | 11|
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**Class imbalance**: 7% / 93% (170 / 2414)
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---
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@ -470,7 +528,14 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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```
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**Class imbalance**: 39% / 61%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|integer | 2|
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|numeric | 55|
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**Class imbalance**: 39% / 61% (1813 / 2788)
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---
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@ -511,7 +576,14 @@ P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferen
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```
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**Class imbalance**: 37% / 63%
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**Predictors**:
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|Class | Frequency|
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|:-------|---------:|
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|factor | 1|
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|numeric | 11|
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**Class imbalance**: 37% / 63% (2384 / 4113)
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---
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@ -64,11 +64,23 @@ for (dir.name in dir(PATH_DATASETS))
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cat(str(dataset))
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cat("\n```\n\n")
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cat("**Predictors**:\n\n")
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df.pred = data.frame(table(sapply(dataset[, 1:(ncol(dataset)-1)],
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function(f){paste(class(f), collapse=" ")})))
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colnames(df.pred) = c("Class", "Frequency")
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cat(knitr::kable(df.pred, format="markdown"), sep="\n")
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cat("\n")
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perc.classes = sort(round(100*as.numeric(
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table(dataset[, ncol(dataset)]))/nrow(dataset), 0))
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num.classes = sort(as.numeric(table(dataset[, ncol(dataset)])))
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cat(paste("**Class imbalance**:",
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paste0(perc.classes[1], "% / ",
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perc.classes[2], "%\n\n")))
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perc.classes[2], "% (",
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num.classes[1], " / ",
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num.classes[2], ")\n\n")))
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cat("---\n\n")
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}
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```
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