bagging   59

« earlier    

Ensemble methods: bagging, boosting and stacking – Towards Data Science
Our last articles with Baptiste Rocca:

Handling imbalanced datasets in machine learning

What should and should not be done when facing an imbalanced classes problem?
A brief introduction to Markov chains

Definitions, properties and PageRank example.
machine-learning  ensemble  bagging  boosting  stacking  overview 
5 weeks ago by ccorbi
Bagging Predictors
Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class.
Breiman  RandomForest  Bagging 
september 2018 by burrowsclayton
Bagging / Bootstrap Aggregation with R
Practical walkthroughs on machine learning, data exploration and finding insight.
bagging  bootstrap  aggregation  machinelearning  tutorial  blog  R 
september 2017 by areich
What are the differences between Random Forest and Gradient Tree Boosting algorithms?
How random forest algorithm maps to bagging and gradient boosting maps to boosting and how both ensemble methods trade off bias and variance in different ways.
machine-learning  boosting  bagging  random-forests  decision-trees  algorithms  bias  variance 
october 2016 by ntraft
Lead Nurturing, Fast and Slow - by @kellblog
"While there is a strong argument that buyers should be nurtured before, during, and after the initial sale, I’m going to speak in this post about pre-sales lead nurturing, the purpose of which is to turn prospective buyers into marketing qualified leads, or MQLs."
content  marketing  startups  demandgen  lead  nurture  mql  speed  bagging  unsubscribe 
august 2016 by jonerp

« earlier    

related tags

1996  a_bendaizer  actor  after  aggregating  aggregation  algorithm  algorithms  analytics  article  arxiv  bag  bagger  bias-variance  bias  big-data  bigdata  blog  bookmarking  boosting  boostrap  bootstrap  bootstrapping  box  boxer  boxing  breiman  brilliant  c++  c4.5  checkout  classification  classifiers  clustering  code  community  computer-science  content  corpora  counter  crossvalidation  data-mining  data  data_analysis  data_science  datamining  decision-trees  decision  decisiontree  defend  demandgen  dropout  ensamble-learning  ensemble-learning  ensemble-methods  ensemble  error  ex-‘cosby’  finance  fly  forward-selection  generalization  geo  geoffrey  glm  groceries  hep  hiking  him  howto  imbalanced  ir  jacket  kifi  large  later  lead  lib  lining  machine-learning  machine_learning  machinelearning  marketing  master_class_data_science  methods  michael-jordan  ml  model-ensembles  model-selection  modeling  mql  neural-net  neuralnetwork  nurture  of  optimization  outdoors  overfitting  overview  owens  paper  papers  peak  pedagogy  people  photo  pluralityvote  posts  predictionmodels  predictive-analytics  predictiveanalytics  r-project  r  random-forests  randomforest  randomforests  randomization  realtime  regularization  research-article  research  retail  rglm  saleman  samoa  scaling  science  sewing  sharing  socialmedia  someone  speed  spr  spss  stacking  startups  statcomp  statistics  storm  streaming  tails  tips  todo  tools  tree  trips  tutorial  unsubscribe  variance  video  voting  washington  wax  web2.0  wharton  wikipedia  wing  wop  wrap  xgboost  yahoo  youtube 

Copy this bookmark: