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ML algorithms
qeAdaBoost(): Ada Boosting, wraps Jousboost pkg
qeDeepnet(): wraps deepnet pkg
qeDT(): decision trees, wraps party pkg
qeGBoost(): gradient boosting, wraps gbm pkg
qeKNN(): k-Nearest Neighbors, wraps regtools pkg; includes predictor importance settings; allows linear interpolation within a bin
qeLASSO(): LASSO and ridge regression, wraps glmment pkg
qeLightGBoost(): gradient boosting, wraps lightgbm pkg
qeliquidSVM: wraps liquidSVM pkg
qeLin(): wraps R’s lm()
qeLinKNN(): first fits qeLin(), followed by k-NN on the residuals to correct deviations from linearity
qeLogit(): wraps R’s glm()
qeNCVregCV: wraps ncvreg package, linear gen. linear regression regularized via SCAD etc.
qeNeural(): wraps keras package, including CNN
qePolyLASSO(): LASSO/ridge applied to polynomial regression; wraps glmnet, polyreg pkgs
qePolyLin(): polynomial regression on linear models; uses Moore-Penrose inverse if overfitting; wraps polyreg pkg
qePolyLog(): polynomial regression on logistic models; wraps polyreg pkg
qeRF(): random forests, wraps randomforest pkg
qeRFgrf: random forests, wraps grf pkg; allows linear interpolation within a bin
qeRpart(): decision trees, wraps Rpart pkg; colorful tree plot
qeRFranger(): random forests, wraps ranger pkg
qeskRF(): random forests, wraps Python Scilearn pkg
qeskSVM(): SVM, wraps Python Scilearn pkg
qeSVM(): SVM, wraps e1071 pkg
qeSVMliquid(): SVM, wraps liquid SVM pkg
qeXGBoost() wraps the xgboost pkg
feature importance/selection
qeFOCI(), qeFOCIrand(): fully nonparametric method for feature selection
qeLASSO(): for feature importance, apply coef() to return value
qeLeaveOut1Var: fits full model, then with all features but 1, for each feature, reporting difference in predictive power; use with any qeML predictive function
qeRareLevels(): investigates whether rare levels of a feature that is an R factor should be included
qeRFranger: variable.importance component of return value
model development
doubleD(): computation and plotting for exploring Double Descent
plotClassesUMAP(): plot first two UMAP components, color-coding classes
plotPairedResiduals(): plot residuals against pairs of features
qeCompare(): compare the accuracy various ML methods on a given dataset
qeFT(): automated grid hyperparameter search, with Bonferroni-Dunn corrected standard errors
qePCA(): find principal components, number specified by user, then fit the resulting model, according to qe* function specified by user
qeROC(): ROC computation and plotting, wraps pROC pkg
qeUMAP(): same as qePCA() but using UMAP
replicMeans(): (from regtools, included in qeML) averages output, e.g. testAcc, over many holdout sets
application-specific functions (elementary)
qeText() text classification
qeTS(): time series
prediction with missing values
utilities, exploratory tools
cartesianFactor(): with inputs of R factors of n1, n2… levels, creates a combined “superfactor” of n1n2… levels
dataToTopLevels(): applies factorToTopLevels() to all fadtors in the given data frame
factorToTopLevels(): removes rare levels from a factor
levelCounts(): performs a census of levels for each R factor in the dataset
newDFRow(): creates a new case to input to predict()
qeParallel(): apply “Software Alchemy” to parallelize qe functions
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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