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

UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

[ICLR 2021 Spotlight Oral] "Undistillable: Making A Nasty Teacher That CANNOT teach students", Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang

Model-based Reinforcement Learning Improves Autonomous Racing Performance

Group Fisher Pruning for Practical Network Compression(ICML2021)

PyTorch-Multi-Style-Transfer - Neural Style and MSG-Net

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"

we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks.

A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation

Generative Flow Networks for Discrete Probabilistic Modeling

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

A time series processing library

PyTorch Implementation of DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs

dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ)

PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE).

2D Human Pose estimation using transformers. Implementation in Pytorch

Hand-distance-measurement-game - Hand Distance Measurement Game

Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning

Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker

PyTorch implementation of DCT fast weight RNNs