R-universe search: exports:Prediction

Showing 5 of total 5 results (show query)

mlr-org

mlr3:Machine Learning in R - Next Generation

Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.

Maintained by Marc Becker. Last updated 18 days ago.

classification data-science machine-learning mlr3 regression

972 stars 14.86 score 2.3k scripts 35 dependents

drordas

D2MCS:Data Driving Multiple Classifier System

Provides a novel framework to able to automatically develop and deploy an accurate Multiple Classifier System based on the feature-clustering distribution achieved from an input dataset. 'D2MCS' was developed focused on four main aspects: (i) the ability to determine an effective method to evaluate the independence of features, (ii) the identification of the optimal number of feature clusters, (iii) the training and tuning of ML models and (iv) the execution of voting schemes to combine the outputs of each classifier comprising the Multiple Classifier System.

Maintained by Miguel Ferreiro-Díaz. Last updated 3 years ago.

openjdk

3.70 score

tbaghfalaki

UHM:Unified Zero-Inflated Hurdle Regression Models

Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024) <doi:...>.

Maintained by Taban Baghfalaki. Last updated 5 months ago.

jags cpp

3.18 score

cran

lmviz:A Package to Visualize Linear Models Features and Play with Them

Contains a suite of shiny applications meant to explore linear model inference feature through simulation and games.

Maintained by Francesco Pauli. Last updated 5 years ago.

1.70 score

hoshmandcomputer

GACFF:Genetic Similarity in User-Based Collaborative Filtering

The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method. By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed. This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others. First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user. By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions. The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.

Maintained by Farimah Houshmand Nanehkaran. Last updated 5 years ago.

1 stars 1.00 score