This function uses individual or an ensemble of classifiers to predict interactions from CF-MS data. This ensemble algorithm combines different results generated from individual classifiers within the ensemble via average to enhance prediction.
ensemble_model(
features,
gd,
classifier = c("glm", "svmRadial", "ranger"),
cv_fold = 2,
verboseIter = TRUE,
plots = FALSE,
filename = file.path(tempdir(), "plots.pdf")
)Ensemble_training_output
prediction score - Prediction scores for whole dataset from each individual classifier.
Best - Selected hyper parameters.
Parameter range - Tested hyper parameters.
prediction_score_test - Scores probabilities for test data from each individual classifier.
class_label - Class probabilities for test data from each individual classifier.
classifier_performance
cm - A confusion matrix.
ACC - Accuracy.
SE - Sensitivity.
SP - Specificity.
PPV - Positive Predictive Value.
F1 - F1-score.
MCC - Matthews correlation coefficient.
Roc_Object - A list of elements.
See roc for more details.
PR_Object - A list of elements.
See pr.curve for more details.
predicted_interactions - The input data frame of pairwise interactions, including classifier scores averaged across all models.
A data frame with protein-protein associations in the first column, and features to be passed to the classifier in the remaining columns.
A gold reference set including true associations with class labels indicating if such PPIs are positive or negative.
The type of classifier to use. See caret
for the available classifiers.
Number of partitions for cross-validation; defaults to 5.
Logical value, indicating whether to check the status of training process;defaults to FALSE.
Logical value, indicating whether to plot the performance of ensemble learning algorithm as compared to individual classifiers; defaults to FALSE.If the argument set to TRUE, plots will be saved in the current working directory. These plots are :
pr_plot - Precision-recall plot of ensemble classifier vs selected individual classifiers.
roc_plot - ROC plot of ensemble classifier vs selected individual classifiers.
points_plot - Plot accuracy, F1-score ,positive predictive value (PPV),sensitivity (SE), and Matthews correlation coefficient (MCC) of ensemble classifier vs selected individual classifiers.
.
character string, indicating the location and output pdf filename for for performance plots. Defaults to tempdir().
Matineh Rahmatbakhsh, matinerb.94@gmail.com
ensemble_model