Metrics: Evaluation Metrics for Machine Learning

Canonical link: https://scholar.rpkg.net/pkg/Metrics

Ben Hamner • Michael Frasco • Erin LeDell • View description and downloads View dependencies GitHub project

2012 Published

0.1.4 Version

0 Citations

3 Authors

Referenced by ⇅	Year
SPOTMisc: Misc Extensions for the "SPOT" Package (Version 1.19.52)	2021
VARMER: Variational Merging (Version 1.0.0)	2021
workboots: Generate Bootstrap Prediction Intervals from a "tidymodels" Workflow (Version 0.2.0)	2022
ARGOS: Automatic Regression for Governing Equations (ARGOS) (Version 0.1.1)	2023
ConsReg: Fits Regression & ARMA Models Subject to Constraints to the Coefficient (Version 0.1.0)	2020
Greymodels: Shiny App for Grey Forecasting Model (Version 2.0.1)	2022
ImFoR: Non-Linear Height Diameter Models for Forestry (Version 0.1.0)	2023
MetaIntegrator: Meta-Analysis of Gene Expression Data (Version 2.1.3)	2016
OptiSembleForecasting: Optimization Based Ensemble Forecasting Using MCS Algorithm (Version 0.1.0)	2022
PUPAIM: A Collection of Physical and Chemical Adsorption Isotherm Models (Version 0.3.1)	2019
PUPAK: Parameter Estimation, and Plot Visualization of Adsorption Kinetic Models (Version 0.1.1)	2022
PUPMSI: Moisture Sorption Isotherm Modeling Program (Version 0.1.0)	2022
PWEV: PSO Based Weighted Ensemble Algorithm for Volatility Modelling (Version 0.1.0)	2024
RSCAT: Shadow-Test Approach to Computerized Adaptive Testing (Version 1.1.3)	2019
RSP: 'shiny' Applications for Statistical and Psychometric Analysis (Version 0.4)	2023
SAMprior: Self-Adapting Mixture (SAM) Priors (Version 1.1.1)	2023
WaveletANN: Wavelet ANN Model (Version 0.1.2)	2019
WaveletETS: Wavelet Based Error Trend Seasonality Model (Version 0.1.0)	2023
WaveletGBM: Wavelet Based Gradient Boosting Method (Version 0.1.0)	2023
WaveletKNN: Wavelet Based K-Nearest Neighbor Model (Version 0.1.0)	2023
audrex: Automatic Dynamic Regression using Extreme Gradient Boosting (Version 2.0.1)	2021
coursekata: Packages and Functions for 'CourseKata' Courses (Version 0.17.0)	2023
cv: Cross-Validating Regression Models (Version 2.0.0)	2023
dblr: Discrete Boosting Logistic Regression (Version 0.1.0)	2017
epicasting: Ewnet: An Ensemble Wavelet Neural Network for Forecasting and Epicasting (Version 0.1.0)	2023
featurefinder: Feature Finder (Version 1.1)	2018
gbm.auto: Automated Boosted Regression Tree Modelling and Mapping Suite (Version 2023.08.31)	2021
hybridts: Hybrid Time Series Forecasting Using Error Remodeling Approach (Version 0.1.0)	2023
iml: Interpretable Machine Learning (Version 0.11.3)	2018
immuneSIM: Tunable Simulation of B- And T-Cell Receptor Repertoires (Version 0.8.7)	2019
janus: Optimized Recommending System Based on 'tensorflow' (Version 1.0.0)	2022
kssa: Known Sub-Sequence Algorithm (Version 0.0.1)	2022
lilikoi: Metabolomics Personalized Pathway Analysis Tool (Version 2.1.1)	2018
luz: Higher Level 'API' for 'torch' (Version 0.4.0)	2021
manymodelr: Build and Tune Several Models (Version 0.3.7)	2019
mlr3shiny: Machine Learning in 'shiny' with 'mlr3' (Version 0.3.0)	2020
phytoclass: Estimate Chla Concentrations of Phytoplankton Groups (Version 1.2.0)	2023
poolHelper: Simulates Pooled Sequencing Genetic Data (Version 1.1.0)	2023
populR: Population Downscaling Using Areal Interpolation (Version 0.2.1)	2021
predtoolsTS: Time Series Prediction Tools (Version 0.1.1)	2017
previsionio: 'Prevision.io' R SDK (Version 11.7.0)	2021
s2net: The Generalized Semi-Supervised Elastic-Net (Version 1.0.7)	2020
sense: Automatic Stacked Ensemble for Regression Tasks (Version 1.1.0)	2021
sjSDM: Scalable Joint Species Distribution Modeling (Version 1.0.5)	2022
superml: Build Machine Learning Models Like Using Python's Scikit-Learn Library in R (Version 0.5.7)	2018
tfdatasets: Interface to 'TensorFlow' Datasets (Version 2.9.0)	2018

RPKG Scholar presents a tabulation of an author's contribution in the development of R packages stored in the Comprehensive R Archive Network (CRAN). Within this site, we consider package dependencies (suggests,imports,depends,enhances) as citations because we believe that using one's package to develop another is tantamount to citing the author of the package being imported, suggested or enhanced.