This package includes functions and data accompanying the book "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
|Date of publication||2013-08-08 19:46:37|
|Maintainer||Luis Torgo <email@example.com>|
|License||GPL (>= 2)|
algae: Training data for predicting algae blooms
algae.sols: The solutions for the test data set for predicting algae...
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...
compExp-class: Class "compExp"
CRchart: Plot a Cumulative Recall chart
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()'
DMwR-defunct: Defunct Functions in Package 'DMwR'
DMwR-package: Functions and data for the book "Data Mining with R"
dsNames: Obtain the name of the data sets involved in an experimental...
experimentalComparison: Carry out Experimental Comparisons Among Learning Systems
expSettings-class: Class "expSettings"
getFoldsResults: Obtain the results on each iteration of a learner
getSummaryResults: Obtain a set of descriptive statistics of the results of a...
getVariant: Obtain the learner associated with an identifier within a...
growingWindowTest: Obtain the predictions of a model using a growing window...
GSPC: A set of daily quotes for SP500
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
knneigh.vect: An auxiliary function of 'lofactor()'
knnImputation: Fill in NA values with the values of the nearest neighbours
learner-class: Class "learner"
learnerNames: Obtain the name of the learning systems involved in an...
LinearScaling: Normalize a set of continuous values using a linear scaling
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
PRcurve: Plot a Precision/Recall curve
prettyTree: Visual representation of a tree-based model
rankSystems: Provide a ranking of learners involved in an experimental...
reachability: An auxiliary function of 'lofactor()'
regr.eval: Calculate Some Standard Regression Evaluation Statistics
ReScaling: Re-scales a set of continuous values into a new range using a...
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
SelfTrain: Self train a model on semi-supervised data
sigs.PR: Precision and recall of a set of predicted trading signals
slidingWindowTest: Obtain the predictions of a model using a sliding window...
SMOTE: SMOTE algorithm for unbalanced classification problems
SoftMax: Normalize a set of continuous values using SoftMax
statNames: Obtain the name of the statistics involved in an experimental...
statScores: Obtains a summary statistic of one of the evaluation metrics...
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"
tradingEvaluation: Obtain a set of evaluation metrics for a set of trading...
trading.signals: Discretize a set of values into a set of trading signals
trading.simulator: Simulate daily trading using a set of trading signals
ts.eval: Calculate Some Standard Evaluation Statistics for Time Series...
unscale: Invert the effect of the scale function
variants: Generate variants of a learning system