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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 13:55:00.
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Document generation date: 2016-04-16 14:20:08.
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@ -28,13 +28,13 @@ Document generation date: 2016-04-16 13:55:00.
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)
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**Files**:
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**Source data files**:
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* [bank-additional.zip](https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip)
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**Cite**:
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```nohighlight
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[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
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S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
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```
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**Dataset**:
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@ -64,14 +64,19 @@ Document generation date: 2016-04-16 13:55:00.
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 11 % | 89 % |
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| 4254 | 33973 |
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---
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@ -81,7 +86,7 @@ Document generation date: 2016-04-16 13:55:00.
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)
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**Files**:
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**Source data files**:
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* [wdbc.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data)
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* [wdbc.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names)
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@ -132,11 +137,16 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 37 % | 63 % |
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| 212 | 357 |
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---
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@ -146,7 +156,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29)
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**Files**:
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**Source data files**:
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* [breast-cancer-wisconsin.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data)
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* [breast-cancer-wisconsin.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.names)
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@ -175,11 +185,16 @@ O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 35 % | 65 % |
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| 239 | 444 |
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---
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@ -189,7 +204,7 @@ O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Cardiotocography)
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**Files**:
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**Source data files**:
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* [CTG.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00193/CTG.xls)
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@ -237,14 +252,19 @@ Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of C
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 22 % | 78 % |
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| 471 | 1655 |
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---
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@ -254,7 +274,7 @@ Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of C
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients)
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**Files**:
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**Source data files**:
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* [default of credit card clients.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls)
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@ -296,12 +316,17 @@ Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 22 % | 78 % |
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| 6636 | 23364 |
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---
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@ -311,7 +336,7 @@ Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset))
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**Files**:
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**Source data files**:
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* [Indian Liver Patient Dataset (ILPD).csv](https://archive.ics.uci.edu/ml/machine-learning-databases/00225/Indian%20Liver%20Patient%20Dataset%20(ILPD).csv)
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@ -341,13 +366,18 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 29 % | 71 % |
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| 167 | 416 |
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---
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@ -357,7 +387,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope)
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**Files**:
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**Source data files**:
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* [magic04.data](https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.data)
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* [magic04.names](https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.names)
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@ -388,11 +418,16 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 35 % | 65 % |
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| 6688 | 12332 |
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---
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@ -402,7 +437,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/seismic-bumps)
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**Files**:
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**Source data files**:
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* [seismic-bumps.arff](https://archive.ics.uci.edu/ml/machine-learning-databases/00266/seismic-bumps.arff)
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@ -436,12 +471,17 @@ Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of d
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 7 % | 93 % |
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| 170 | 2414 |
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---
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@ -451,7 +491,7 @@ Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of d
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Spambase)
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**Files**:
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**Source data files**:
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* [spambase.DOCUMENTATION](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.DOCUMENTATION)
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* [spambase.data](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data)
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@ -530,12 +570,17 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 39 % | 61 % |
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| 1813 | 2788 |
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---
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@ -545,7 +590,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)
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**Files**:
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**Source data files**:
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* [winequality-red.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv)
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* [winequality-white.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv)
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@ -578,12 +623,17 @@ P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferen
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**Predictors**:
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|Class | Frequency|
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|Type | 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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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 37 % | 63 % |
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| 2384 | 4113 |
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---
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@ -7,4 +7,4 @@ urls:
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- https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip
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cite: >
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[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
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S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
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@ -44,7 +44,7 @@ for (dir.name in dir(PATH_DATASETS))
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cat(paste0("**Details**: [link](", config.yaml$info, ")\n\n"))
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cat(paste("**Files**:\n\n"))
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cat(paste("**Source data files**:\n\n"))
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for (file.url in config.yaml$urls)
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{
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cat(paste0("* [", URLdecode(basename(file.url)), "](", file.url, ")\n"))
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@ -68,7 +68,7 @@ for (dir.name in dir(PATH_DATASETS))
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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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colnames(df.pred) = c("Type", "Frequency")
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cat(knitr::kable(df.pred, format="markdown"), sep="\n")
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cat("\n")
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@ -76,12 +76,17 @@ for (dir.name in dir(PATH_DATASETS))
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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], "% (",
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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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cat("**Class imbalance**:\n\n")
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cat(knitr::kable(data.frame(A=c(paste(perc.classes[1], "%"), num.classes[1]),
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B=c(paste(perc.classes[2], "%"), num.classes[2])),
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format="markdown", col.names=c("class A", " class B"),
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align=c("c", "c")),
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sep="\n")
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cat("\n---\n\n")
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}
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```
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