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

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

This library provides an abstraction to perform Model Versioning using Weight & Biases.

Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021)

The official homepage of the (outdated) COCO-Stuff 10K dataset.

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

Source code for "Understanding Knowledge Integration in Language Models with Graph Convolutions"

hipCaffe: the HIP port of Caffe

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Pytorch code for semantic segmentation using ERFNet

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Face recognize and crop them

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Object detection GUI based on PaddleDetection

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

An implementation of DeepMind's Relational Recurrent Neural Networks in PyTorch.

Unofficial PyTorch implementation of Google AI's VoiceFilter system

code for Multi-scale Matching Networks for Semantic Correspondence, ICCV