Linear-complexity Private Function Evaluation (PFE) based on homomorphic encryption (as presented at ESORICS'20).
-
Updated
Sep 14, 2020 - C++
#
performance-measurements
Here are 4 public repositories matching this topic...
Titanic rescue prediction using Decision Tree, SVM, Logistic Regression, Random Forest and KNN. The best accuracy score was from Random Forest: 84.35%
machine-learning
random-forest
numpy
sklearn
pandas
seaborn
titanic-kaggle
logistic-regression
matplotlib
decision-trees
kfold-cross-validation
machine-learning-projects
knn-algorithm
support-vector-classification
performance-measurements
-
Updated
Jun 9, 2021 - Jupyter Notebook
Predicting students admission with Logistic Regression, Decision Tree, SVM (SVC) and Random Forest
machine-learning
random-forest
machine-learning-algorithms
logistic-regression
resampling
decision-tree
prediction-model
svc
kfold-cross-validation
machine-learning-projects
over-sampling
performance-measurements
-
Updated
May 1, 2023 - Jupyter Notebook
A simple, lightweight, and concurrent agent for load simulation and monitoring HTTP test object performance and availability - analyze and monitor performance metrics with configurable virtual users and InfluxDB integration for comprehensive reporting and visualization.
-
Updated
Apr 1, 2023 - Go
Improve this page
Add a description, image, and links to the performance-measurements topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the performance-measurements topic, visit your repo's landing page and select "manage topics."