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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?
towardsdatascience.com
A brief introduction to Markov chains

Definitions, properties and PageRank example.
towardsdatascience.com
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

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