Evaluation of Modeling without Information Leakage

[Up] [Top]

Documentation for package ‘emil’ version 2.2.8

Help Pages

A D E F G H I L M N P R S T V W

-- A --

as.data.frame.roc_curve Calculate ROC curves
as.data.table.roc_curve Calculate ROC curves
as.modeling_procedure Coerce to modeling procedure

-- D --

detune Tune parameters of modeling procedures
dichotomize Dichotomize time-to-event data

-- E --

emil Introduction to the emil package
error_fun Performance estimation functions
error_rate Performance estimation functions
evaluate Evaluate a modeling procedure
extension Extending the emil framework with user-defined methods

-- F --

factor_to_logical Convert factors to logicals
fill Replace values with something else
fit Fit a model
fit_caret Fit a model using the 'caret' package
fit_cforest Fit conditional inference forest
fit_coxph Fit Cox proportional hazards model
fit_glmnet Fit elastic net, LASSO or ridge regression model
fit_lasso Fit elastic net, LASSO or ridge regression model
fit_lda Fit linear discriminant
fit_lm Fit a linear model fitted with ordinary least squares
fit_naive_bayes Fit a naive Bayes classifier
fit_pamr Fit nearest shrunken centroids model.
fit_qda Fit quadratic discriminant.
fit_randomForest Fit random forest.
fit_ridge_regression Fit elastic net, LASSO or ridge regression model
fit_rpart Fit a decision tree
fit_svm Fit a support vector machine

-- G --

get_color Get color palettes
get_color.default Get color palettes
get_color.factor Get color palettes
get_importance Feature (variable) importance of a fitted model
get_performance Extract prediction performance
get_prediction Extract predictions from modeling results
get_response Extract the response from a data set
get_tuning Extract parameter tuning statistics

-- H --

hlines Add vertical or horizontal lines to a plot

-- I --

image.crossvalidation Visualize resampling scheme
image.resample Visualize resampling scheme
importance_glmnet Feature importance extractor for elastic net models
importance_lasso Feature importance extractor for elastic net models
importance_pamr Feature importance of nearest shrunken centroids.
importance_randomForest Feature importance of random forest.
importance_ridge_regression Feature importance extractor for elastic net models
impute Regular imputation
impute_knn Regular imputation
impute_median Regular imputation
indent Increase indentation
index_fit Convert a fold to row indexes of fittdng or test set
index_test Convert a fold to row indexes of fittdng or test set
is_blank Wrapper for several methods to test if a variable is empty
is_constant Check if an object contains more than one unique value
is_multi_procedure Detect if modeling results contains multiple procedures
is_tunable Tune parameters of modeling procedures
is_tuned Tune parameters of modeling procedures

-- L --

learning_curve Learning curve analysis
list_method List all available methods
log_message Print a timestamped and indented log message

-- M --

mode Get the most common value
modeling_procedure Setup a modeling procedure
mse Performance estimation functions

-- N --

name_procedure Get names for modeling procedures
na_fill Replace values with something else
na_index Support function for identifying missing values
neg_auc Performance estimation functions
neg_gmpa Negative geometric mean of class specific predictive accuracy
neg_harrell_c Performance estimation functions
nice_axis Plots an axis the way an axis should be plotted.
nice_box Plots a box around a plot
nice_require Load a package and offer to install if missing
notify_once Print a warning message if not printed earlier

-- P --

plot.learning_curve Plot results from learning curve analysis
plot.roc_curve Calculate ROC curves
plot.Surv Plot Surv vector
predict.model Predict the response of unknown observations
predict_caret Predict using a 'caret' method
predict_cforest Predict with conditional inference forest
predict_coxph Predict using Cox proportional hazards model
predict_glmnet Predict using generalized linear model with elastic net regularization
predict_lasso Predict using generalized linear model with elastic net regularization
predict_lda Prediction using already trained prediction model
predict_lm Prediction using linear model
predict_naive_bayes Predict using naive Bayes model
predict_pamr Prediction using nearest shrunken centroids.
predict_qda Prediction using already trained classifier.
predict_randomForest Prediction using random forest.
predict_ridge_regression Predict using generalized linear model with elastic net regularization
predict_rpart Predict using a fitted decision tree
predict_svm Predict using support vector machine
pre_center Data preprocessing
pre_convert Data preprocessing
pre_factor_to_logical Convert factors to logical columns
pre_impute Basic imputation
pre_impute_df Impute a data frame
pre_impute_knn Nearest neighbors imputation
pre_impute_mean Basic imputation
pre_impute_median Basic imputation
pre_log_message Print log message during pre-processing
pre_pamr PAMR adapted dataset pre-processing
pre_pca Data preprocessing
pre_process Data preprocessing
pre_remove Data preprocessing
pre_remove_constant Data preprocessing
pre_remove_correlated Data preprocessing
pre_scale Data preprocessing
pre_split Data preprocessing
pre_transpose Data preprocessing
print.preprocessed_data Print method for pre-processed data
pvalue Extraction of p-value from a statistical test
pvalue.coxph Extract p-value from a Cox proportional hazards model
pvalue.crr Extracts p-value from a competing risk model
pvalue.cuminc Extract p-value from a cumulative incidence estimation
pvalue.survdiff Extracts p-value from a logrank test

-- R --

resample Resampling schemes
resample_bootstrap Resampling schemes
resample_crossvalidation Resampling schemes
resample_holdout Resampling schemes
reset_notification Print a warning message if not printed earlier
rmse Performance estimation functions
roc_curve Calculate ROC curves

-- S --

select 'emil' and 'dplyr' integration
select_.list 'emil' and 'dplyr' integration
subresample Generate resampling subschemes
subtree Extract a subset of a tree of nested lists

-- T --

trivial_error_rate Calculate the trivial error rate
tune Tune parameters of modeling procedures

-- V --

validate_data Validate a pre-processed data set
vlines Add vertical or horizontal lines to a plot

-- W --

weighted_error_rate Weighted error rate