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Model Selection

69 papers with code · Methodology

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Scikit-learn: Machine Learning in Python

2 Jan 2012scikit-learn/scikit-learn

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.

DIMENSIONALITY REDUCTION MODEL SELECTION

Tune: A Research Platform for Distributed Model Selection and Training

13 Jul 2018ray-project/ray

We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation.

HYPERPARAMETER OPTIMIZATION MODEL SELECTION

Easy Transfer Learning By Exploiting Intra-domain Structures

2 Apr 2019jindongwang/transferlearning

In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.

MODEL SELECTION TRANSFER LEARNING

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms

SCIPY 2013 2013 hyperopt/hyperopt

Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization.

HYPERPARAMETER OPTIMIZATION MODEL SELECTION

metric-learn: Metric Learning Algorithms in Python

13 Aug 2019scikit-learn-contrib/metric-learn

metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms.

METRIC LEARNING MODEL SELECTION

Data-driven Advice for Applying Machine Learning to Bioinformatics Problems

8 Aug 2017rhiever/sklearn-benchmarks

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms.

MODEL SELECTION

Cold Case: The Lost MNIST Digits

25 May 2019facebookresearch/qmnist

Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lost to time.

IMAGE CLASSIFICATION MODEL SELECTION

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

NeurIPS 2018 kirthevasank/nasbot

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

BAYESIAN OPTIMISATION MODEL SELECTION NEURAL ARCHITECTURE SEARCH