Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes

Last update: Dec 15, 2021

Related tags

Deep Learning cv-pruner

Overview

Pruner for nested cross-validation - Sphinx-Doc

Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes. Standard pruning algorithms to accelerate hyperparameter optimization must prune late or risk aborting computations of promising hyperparameter sets due to high variance in the performance evaluation metric. The cv-pruner allows combining a comparison-based pruning strategy with two additional pruning strategies based on domain or prior knowledge. One of them prunes semantically meaningless trials. The other is a threshold-based pruning strategy that extrapolates the performance evaluation metric. The combination of pruning strategies can lead to a massive speedup in computation.

Installation

CV-Pruner is available at the Python Package Index (PyPI). It can be installed with pip:

$ pip install cv-pruner

Licensing

Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License by reviewing the file LICENSE in the repository.

The implementation for paper Joint t-SNE for Comparable Projections of Multiple High-Dimensional Datasets.

Joint t-sne This is the implementation for paper Joint t-SNE for Comparable Projections of Multiple High-Dimensional Datasets. abstract: We present Jo

7 Dec 18, 2022

Genetic feature selection module for scikit-learn

sklearn-genetic Genetic feature selection module for scikit-learn Genetic algorithms mimic the process of natural selection to search for optimal valu

260 Dec 14, 2022

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici

6 Dec 8, 2022

Defending against Model Stealing via Verifying Embedded External Features

Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

dcf-game-infrastructure All the components necessary to run a game of the OOO DC

46 Sep 13, 2022

This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

2 Oct 5, 2022

Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes

Related tags

Overview

Pruner for nested cross-validation - Sphinx-Doc

Installation

Licensing

You might also like...

The implementation for paper Joint t-SNE for Comparable Projections of Multiple High-Dimensional Datasets.

Genetic feature selection module for scikit-learn

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Defending against Model Stealing via Verifying Embedded External Features

The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

Exploring whether attention is necessary for vision transformers

Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Releases(0.0.1rc3)

0.0.1rc3(Nov 22, 2021)

0.0.1rc2(Nov 19, 2021)

Owner

GDSC-ML Team Interview Task

Repository of 3D Object Detection with Pointformer (CVPR2021)

A little software to generate and save Julia or Mandelbrot's Fractals.

A pre-trained language model for social media text in Spanish

OntoProtein: Protein Pretraining With Ontology Embedding

for taichi voxel-challange event

Bling's Object detection tool

Automatic differentiation with weighted finite-state transducers.

Release of the ConditionalQA dataset

RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

A unet implementation for Image semantic segmentation

PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning

Source code for our paper "Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash"

PyTorch-centric library for evaluating and enhancing the robustness of AI technologies

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data - Official PyTorch Implementation (CVPR 2022)

CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Code for "Learning Graph Cellular Automata"

Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))