Model Selection

231 papers with code • 0 benchmarks • 1 datasets

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Greatest papers with code

Scikit-learn: Machine Learning in Python

scikit-learn/scikit-learn 2 Jan 2012

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 General Classification +1

Tune: A Research Platform for Distributed Model Selection and Training

ray-project/ray 13 Jul 2018

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 +1

Easy Transfer Learning By Exploiting Intra-domain Structures

jindongwang/transferlearning 2 Apr 2019

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

Domain Adaptation Model Selection +1

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

hyperopt/hyperopt SCIPY 2013 2013

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

Familia: An Open-Source Toolkit for Industrial Topic Modeling

baidu/Familia 31 Jul 2017

Familia is an open-source toolkit for pragmatic topic modeling in industry.

Model Selection Topic Models

metric-learn: Metric Learning Algorithms in Python

all-umass/metric_learn 13 Aug 2019

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

Metric Learning Model Selection

An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization

huawei-noah/vega 11 Jul 2020

The evaluation of hyperparameters, neural architectures, or data augmentation policies becomes a critical model selection problem in advanced deep learning with a large hyperparameter search space.

Data Augmentation Hyperparameter Optimization +3

EPP: interpretable score of model predictive power

ModelOriented/DrWhy 24 Aug 2019

Second is, that for k-fold cross-validation, the model performance is in most cases calculated as an average performance from particular folds, which neglects the information how stable is the performance for different folds.

General Classification Model Selection