DMwR: Functions and data for "Data Mining with R"
データマイニング用関数とデータ
- CRAN: https://cran.r-project.org/web/packages/DMwR/index.html
- URL: http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR/book.html
> library(DMwR)
Loading required package: lattice
Loading required package: grid
バージョン: 0.4.1
| . |
|---|
| CRchart Plot a Cumulative Recall chart |
| DMwR-package Functions and data for the book "Data Mining |
| with R" |
| GSPC A set of daily quotes for SP500 |
| LinearScaling Normalize a set of continuous values using a |
| linear scaling |
| PRcurve Plot a Precision/Recall curve |
| ReScaling Re-scales a set of continuous values into a new |
| range using a linear scaling |
| SMOTE SMOTE algorithm for unbalanced classification |
| problems |
| SelfTrain Self train a model on semi-supervised data |
| SoftMax Normalize a set of continuous values using |
| SoftMax |
| algae Training data for predicting algae blooms |
| algae.sols The solutions for the test data set for |
| predicting algae blooms |
| bestScores Obtain the best scores from an experimental |
| comparison |
| bootRun-class Class "bootRun" |
| bootSettings-class Class "bootSettings" |
| bootstrap Runs a bootstrap experiment |
| centralImputation Fill in NA values with central statistics |
| centralValue Obtain statistic of centrality |
| class.eval Calculate Some Standard Classification |
| Evaluation Statistics |
| compAnalysis Analyse and print the statistical significance |
| of the differences between a set of learners. |
| compExp-class Class "compExp" |
| crossValidation Run a Cross Validation Experiment |
| cvRun-class Class "cvRun" |
| cvSettings-class Class "cvSettings" |
| dataset-class Class "dataset" |
| dist.to.knn An auxiliary function of 'lofactor()' |
| dsNames Obtain the name of the data sets involved in an |
| experimental comparison |
| expSettings-class Class "expSettings" |
| experimentalComparison |
| Carry out Experimental Comparisons Among |
| Learning Systems |
| getFoldsResults Obtain the results on each iteration of a |
| learner |
| getSummaryResults Obtain a set of descriptive statistics of the |
| results of a learner |
| getVariant Obtain the learner associated with an |
| identifier within a comparison |
| growingWindowTest Obtain the predictions of a model using a |
| growing window learning approach. |
| hldRun-class Class "hldRun" |
| hldSettings-class Class "hldSettings" |
| holdOut Runs a Hold Out experiment |
| join Merging several 'compExp' class objects |
| kNN k-Nearest Neighbour Classification |
| knnImputation Fill in NA values with the values of the |
| nearest neighbours |
| knneigh.vect An auxiliary function of 'lofactor()' |
| learner-class Class "learner" |
| learnerNames Obtain the name of the learning systems |
| involved in an experimental comparison |
| lofactor An implementation of the LOF algorithm |
| loocv Run a Leave One Out Cross Validation Experiment |
| loocvRun-class Class "loocvRun" |
| loocvSettings-class Class "loocvSettings" |
| manyNAs Find rows with too many NA values |
| mcRun-class Class "mcRun" |
| mcSettings-class Class "mcSettings" |
| monteCarlo Run a Monte Carlo experiment |
| outliers.ranking Obtain outlier rankings |
| prettyTree Visual representation of a tree-based model |
| rankSystems Provide a ranking of learners involved in an |
| experimental comparison. |
| reachability An auxiliary function of 'lofactor()' |
| regr.eval Calculate Some Standard Regression Evaluation |
| Statistics |
| resp Obtain the target variable values of a |
| prediction problem |
| rpartXse Obtain a tree-based model |
| rt.prune Prune a tree-based model using the SE rule |
| runLearner Run a Learning Algorithm |
| sales A data set with sale transaction reports |
| sigs.PR Precision and recall of a set of predicted |
| trading signals |
| slidingWindowTest Obtain the predictions of a model using a |
| sliding window learning approach. |
| statNames Obtain the name of the statistics involved in |
| an experimental comparison |
| statScores Obtains a summary statistic of one of the |
| evaluation metrics used in an experimental |
| comparison, for all learners and data sets |
| involved in the comparison. |
| subset-methods Methods for Function subset in Package 'DMwR' |
| task-class Class "task" |
| test.algae Testing data for predicting algae blooms |
| tradeRecord-class Class "tradeRecord" |
| trading.signals Discretize a set of values into a set of |
| trading signals |
| trading.simulator Simulate daily trading using a set of trading |
| signals |
| tradingEvaluation Obtain a set of evaluation metrics for a set of |
| trading actions |
| ts.eval Calculate Some Standard Evaluation Statistics |
| for Time Series Forecasting Tasks |
| unscale Invert the effect of the scale function |
| variants Generate variants of a learning system |
| 関数名 | 概略 |
|---|---|
CRchart |
Plot a Cumulative Recall chart |
DMwR-package |
Functions and data for the book "Data Mining with R" |
GSPC |
A set of daily quotes for SP500 |
LinearScaling |
Normalize a set of continuous values using a linear scaling |
PRcurve |
Plot a Precision/Recall curve |
ReScaling |
Re-scales a set of continuous values into a new range using a linear scaling |
SMOTE |
SMOTE algorithm for unbalanced classification problems |
SelfTrain |
Self train a model on semi-supervised data |
SoftMax |
Normalize a set of continuous values using SoftMax |
algae |
Training data for predicting algae blooms |
algae.sols |
The solutions for the test data set for predicting algae blooms |
bestScores |
Obtain the best scores from an experimental comparison |
bootRun-class |
Class "bootRun" |
bootSettings-class |
Class "bootSettings" |
bootstrap |
Runs a bootstrap experiment |
centralImputation |
Fill in NA values with central statistics |
centralValue |
Obtain statistic of centrality |
class.eval |
Calculate Some Standard Classification Evaluation Statistics |
compAnalysis |
Analyse and print the statistical significance of the differences between a set of learners. |
compExp-class |
Class "compExp" |
crossValidation |
Run a Cross Validation Experiment |
cvRun-class |
Class "cvRun" |
cvSettings-class |
Class "cvSettings" |
dataset-class |
Class "dataset" |
dist.to.knn |
An auxiliary function of 'lofactor()' |
dsNames |
Obtain the name of the data sets involved in an experimental comparison |
expSettings-class |
Class "expSettings" |
experimentalComparison |
Carry out Experimental Comparisons Among Learning Systems |
getFoldsResults |
Obtain the results on each iteration of a learner |
getSummaryResults |
Obtain a set of descriptive statistics of the results of a learner |
getVariant |
Obtain the learner associated with an identifier within a comparison |
growingWindowTest |
Obtain the predictions of a model using a growing window learning approach. |
hldRun-class |
Class "hldRun" |
hldSettings-class |
Class "hldSettings" |
holdOut |
Runs a Hold Out experiment |
join |
Merging several 'compExp' class objects |
kNN |
k-Nearest Neighbour Classification |
knnImputation |
Fill in NA values with the values of the nearest neighbours |
knneigh.vect |
An auxiliary function of 'lofactor()' |
learner-class |
Class "learner" |
learnerNames |
Obtain the name of the learning systems involved in an experimental comparison |
lofactor |
An implementation of the LOF algorithm |
loocv |
Run a Leave One Out Cross Validation Experiment |
loocvRun-class |
Class "loocvRun" |
loocvSettings-class |
Class "loocvSettings" |
manyNAs |
Find rows with too many NA values |
mcRun-class |
Class "mcRun" |
mcSettings-class |
Class "mcSettings" |
monteCarlo |
Run a Monte Carlo experiment |
outliers.ranking |
Obtain outlier rankings |
prettyTree |
Visual representation of a tree-based model |
rankSystems |
Provide a ranking of learners involved in an experimental comparison. |
reachability |
An auxiliary function of 'lofactor()' |
regr.eval |
Calculate Some Standard Regression Evaluation Statistics |
resp |
Obtain the target variable values of a prediction problem |
rpartXse |
Obtain a tree-based model |
rt.prune |
Prune a tree-based model using the SE rule |
runLearner |
Run a Learning Algorithm |
sales |
A data set with sale transaction reports |
sigs.PR |
Precision and recall of a set of predicted trading signals |
slidingWindowTest |
Obtain the predictions of a model using a sliding window learning approach. |
statNames |
Obtain the name of the statistics involved in an experimental comparison |
statScores |
Obtains a summary statistic of one of the evaluation metrics used in an experimental comparison, for all learners and data sets involved in the comparison. |
subset-methods |
Methods for Function subset in Package 'DMwR' |
task-class |
Class "task" |
test.algae |
Testing data for predicting algae blooms |
tradeRecord-class |
Class "tradeRecord" |
trading.signals |
Discretize a set of values into a set of trading signals |
trading.simulator |
Simulate daily trading using a set of trading signals |
tradingEvaluation |
Obtain a set of evaluation metrics for a set of trading actions |
ts.eval |
Calculate Some Standard Evaluation Statistics for Time Series Forecasting Tasks |
unscale |
Invert the effect of the scale function |
variants |
Generate variants of a learning system |
GSPC
> GSPC %>% class()
[1] "xts" "zoo"
lofactor
局所外れ値度
Arguments
- data
- k
> lofactor(iris[, -5], k = 10)
[1] 0.9749183 0.9933587 0.9971526 1.0082478 0.9976917 1.1171744 1.1367249
[8] 0.9759294 1.2258481 0.9771966 1.0505187 1.0374252 0.9681938 1.4668862
[15] 1.4358094 1.6070560 1.1684473 0.9724836 1.2538291 1.0277455 1.1418438
[22] 0.9978688 1.6511907 1.2168410 1.3520534 1.0752058 1.0133658 0.9848109
[29] 0.9847763 0.9539171 0.9786418 1.1052521 1.2580528 1.3292011 0.9771966
[36] 1.0561318 1.1505964 1.0053093 1.1763094 0.9694070 0.9691967 2.1401892
[43] 1.1634980 1.2963993 1.2733889 0.9681938 1.1023672 1.0025265 1.0224348
[50] 0.9767920 1.0987760 0.9908970 1.0980881 1.0528158 0.9627714 1.0114595
[57] 0.9870990 1.5075267 0.9971008 1.1278327 1.4583470 0.9992419 1.1880586
[64] 0.9777481 1.1343224 1.0262747 1.0379323 0.9597265 1.2802262 1.0303722
[71] 1.0551560 1.0448665 1.0354977 0.9867182 0.9957740 1.0061086 1.0421399
[78] 1.0111163 0.9953478 1.1875462 1.0718922 1.0944867 0.9747153 0.9842419
[85] 1.1455531 1.0785159 1.0441614 1.2076864 0.9735054 1.0045347 1.0227888
[92] 0.9901783 0.9687127 1.4341384 0.9782820 0.9763235 0.9852838 0.9988898
[99] 1.5277244 0.9597168 1.1422904 1.0333302 1.0699243 1.0361851 0.9982689
[106] 1.1366996 1.6907715 1.1523481 1.1793638 1.2761094 1.0222755 1.0112407
[113] 0.9756681 1.1200491 1.2440212 1.0018319 0.9743390 1.2640817 1.2613618
[120] 1.1273150 0.9895024 1.0803134 1.1936765 0.9715100 0.9946723 1.1513666
[127] 0.9796737 0.9919251 0.9685807 1.2152147 1.1421662 1.2609661 0.9704360
[134] 0.9920405 1.2411459 1.1450440 1.0565329 0.9848026 0.9826126 0.9847805
[141] 1.0002989 1.0475650 1.0333302 0.9963329 1.0315736 1.0116648 1.0012104
[148] 0.9991678 1.1183161 0.9910457