MachineLearning

More info on CRAN
Name Description Percentile Stars
ahaz Regularization for semiparametric additive hazards regression 0th
0
arules Mining Association Rules and Frequent Itemsets 0th
0
BayesTree Bayesian Additive Regression Trees 0th
0
bigRR Generalized Ridge Regression (with special advantage for p >> n cases) 0th
0
bmrm Bundle Methods for Regularized Risk Minimization Package 0th
0
Boruta Wrapper Algorithm for All Relevant Feature Selection 0th
0
bst Gradient Boosting 0th
0
C50 C5.0 Decision Trees and Rule-Based Models 0th
0
caret Classification and Regression Training 0th
1
CORElearn Classification, Regression and Feature Evaluation 0th
0
CoxBoost Cox models by likelihood based boosting for a single survival endpoint or competing risks 0th
0
Cubist Rule- And Instance-Based Regression Modeling 0th
0
e1071 Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien 0th
1
earth Multivariate Adaptive Regression Splines 0th
0
elasticnet Elastic Net Regularization and Variable Selection 0th
0
ElemStatLearn Data Sets, Functions and Examples from the Book: "The Elements of Statistical Learning, Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman 0th
0
evtree Evolutionary Learning of Globally Optimal Trees 0th
0
FCNN4R Fast Compressed Neural Networks for R 0th
0
frbs Fuzzy Rule-Based Systems for Classification and Regression Tasks 0th
0
GAMBoost Generalized linear and additive models by likelihood based boosting 0th
0
gamboostLSS Boosting Methods for 'GAMLSS' 0th
0
gbm Generalized Boosted Regression Models 0th
0
glmnet Lasso and Elastic-Net Regularized Generalized Linear Models 0th
1
glmpath L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model 0th
0
GMMBoost Likelihood-based Boosting for Generalized mixed models 0th
0
grplasso Fitting User-Specified Models with Group Lasso Penalty 0th
0
grpreg Regularization Paths for Regression Models with Grouped Covariates 0th
0
h2o R Interface for 'H2O' 0th
0
hda Heteroscedastic Discriminant Analysis 0th
0
hdi High-Dimensional Inference 0th
0
hdm High-Dimensional Metrics 0th
0
ipred Improved Predictors 0th
0
kernlab null 0th
0
klaR Classification and Visualization 0th
0
lars Least Angle Regression, Lasso and Forward Stagewise 0th
0
lasso2 L1 Constrained Estimation aka `lasso' 0th
0
LiblineaR Linear Predictive Models Based on the 'LIBLINEAR' C/C++ Library 0th
0
LogicForest Logic Forest 0th
0
LogicReg Logic Regression 0th
0
maptree Mapping, pruning, and graphing tree models 0th
0
mboost Model-Based Boosting 0th
0
mlr Machine Learning in R 0th
0
ncvreg Regularization Paths for SCAD and MCP Penalized Regression Models 0th
0
nnet Feed-Forward Neural Networks and Multinomial Log-Linear Models 0th
0
oblique.tree Oblique Trees for Classification Data 0th
0
pamr Pam: Prediction Analysis for Microarrays 0th
0
party A Laboratory for Recursive Partytioning 0th
0
partykit A Toolkit for Recursive Partytioning 0th
0
penalized L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model 0th
0
penalizedLDA Penalized Classification using Fisher's Linear Discriminant 0th
0
penalizedSVM Feature Selection SVM using Penalty Functions 0th
0
quantregForest Quantile Regression Forests 0th
0
randomForest Breiman and Cutler's Random Forests for Classification and Regression 0th
1
randomForestSRC Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) 0th
0
ranger A Fast Implementation of Random Forests 0th
0
rattle Graphical User Interface for Data Science in R 0th
0
Rborist Extensible, Parallelizable Implementation of the Random Forest Algorithm 0th
0
rda Shrunken Centroids Regularized Discriminant Analysis 0th
0
rdetools Relevant Dimension Estimation (RDE) in Feature Spaces 0th
0
REEMtree Regression Trees with Random Effects for Longitudinal (Panel) Data 0th
0
relaxo Relaxed Lasso 0th
0
rgenoud R Version of GENetic Optimization Using Derivatives 0th
0
rgp R genetic programming framework 0th
0
Rmalschains Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R 0th
0
rminer Data Mining Classification and Regression Methods 0th
0
ROCR Visualizing the Performance of Scoring Classifiers 0th
0
RoughSets Data Analysis Using Rough Set and Fuzzy Rough Set Theories 0th
0
rpart Recursive Partitioning and Regression Trees 0th
0
RPMM Recursively Partitioned Mixture Model 0th
0
RSNNS Neural Networks using the Stuttgart Neural Network Simulator (SNNS) 0th
0
RWeka R/Weka Interface 0th
0
RXshrink Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression 0th
0
sda Shrinkage Discriminant Analysis and CAT Score Variable Selection 0th
1
SIS Sure Independence Screening 0th
0
stabs Stability Selection with Error Control 0th
0
SuperLearner Super Learner Prediction 0th
0
svmpath The SVM Path Algorithm 0th
1
tgp Bayesian Treed Gaussian Process Models 0th
0
tree Classification and Regression Trees 0th
0
varSelRF Variable Selection using Random Forests 0th
0
vcrpart Tree-Based Varying Coefficient Regression for Generalized Linear and Ordinal Mixed Models 0th
0
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