# Function List

- 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