DMwR: Functions and data for "Data Mining with R"

データマイニング用関数とデータ

> 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