Function List
In qeML: Quick and Easy Machine Learning Tools

title: "Function List" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Function List} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc}

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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
- qeLinMV(), qeLogitMV(), qeKNNMV(), associated predict() generics for use with toweranNA pkg
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