Boosting (machine learning) Puns

Hello everyone. I want to tell you about a convenient and simple way to search for external datasets from around the world that can improve the quality of your ML pipelines.

I often face the need to improve the quality of predictions in my everyday work and at Kaggle competitions. Adding useful external datasets is the most obvious way to do that. Therefore, the dataset search is an important issue for me. And I began to wonder how could I quickly find useful external datasets for my pipelines.

We know how to search data by keywords using Dataset search from Google https://datasetsearch.research.google.com or different data marketplaces. But the search for data by keywords is very inconvenient.

Firstly, it's unclear which words should I enter to find useful data to improve the quality of my prediction in the ML pipeline. What kind of words should I use for churn prediction? And what about purchase prediction or insurance claim prediction?

https://preview.redd.it/0a7zojdr55481.jpg?width=936&format=pjpg&auto=webp&s=288ebb03b2e80ecea7130f90b94db00effb13901

Secondly, hundreds of datasets from different sites are displayed in the results of each keyword search. You need to register on each site to download data, and you need to spend 5-10 minutes on each data source. If you search for data by the keyword "churn prediction", you need to check hundreds of sources, which means that you have to spend thousands of minutes on it. And that's just for one keyword! I know that the competitive situation affects the churn, I go and enter new keywords about competitors and spend thousands of minutes again. It can be seen that the process is very inconvenient and extremely time-consuming.

Thirdly, after downloading some datasets in thousands of minutes you need to spend time to integrate this data into your ML pipeline and measure whether this data gives you uplift or not. Integration is often difficult: sources have different keys and different observation periods. And what a disappointment it can be when you'll see that only a few of the hundreds of downloaded datasets are useful for your model.

How do you think is it possible to achieve the best quality of the model in this way of external datasets search and win any competition? Of course not, because you either won't meet the allotted time, or you won't be able to find valuable data by keywords.

After I finished a few such projects and thought about a

It seems to me that "bagging" was invented before "boosting".

Popular boosting algorithms include : adaboost, extreme gradient boosting (xgboost) and gradient boosting machines (gbm)

It seems that adaboost is the "original default" boosting algorithm - is this correct?

Is there some relationship between gbm and xgboost?

I am trying to use different types of Machine Learning (ML), LASSO, Elastic Net and Boosting, in a dataset with around 6000 observations and 120 regressors. To test the goodness-of-fit of results, I used an out-of-sample technique: I create a subset (training set) and availing the result in the remaining sample (test set) to make the prediction. Next, I have compared the result of this prediction with the real value of the dependent variable. I made it for ten times (training-test trial).

I noted important overfitting. In my opinion, this effect is a consequence of biased estimation in training set in turn caused by zero-inflated values in the sample ( only 22% of the total sample have a value not zero).

I thought to increase the number of observations, simply, making a dataset with 30.000 observation, deriving from the five copies of original observations. To justify this approach, in my view, use a weak Law of Large Numbers (wLLT). The result is much encouragement and the problem of overfitting very limited. I accept every suggests or critiques of this approach to implement my study.

I add that my original database is stratified.

Machine learning has been widely used in finding efficient solutions in the healthcare sector to help in diagnosing and treating various illnesses.

Sarepta Therapeutics, located in Cambridge, Massachusetts, produced a breakthrough drug in 2016 that directly targets the mutated gene responsible for Duchenne muscular dystrophy (DMD). This rare genetic condition weakens muscles in young boys throughout the body until the heart or lungs fail.

This drug uses Antisense phosphorodiamidate morpholino oligomers (PMO), a huge synthetic molecule that permeates the cell nucleus and modifies the dystrophin gene. This allows for the production of a vital protein that is ordinarily absent in DMD patients. PMO, however, is ineffective at entering cells.

Quick Read: https://www.marktechpost.com/2021/08/20/mit-researchers-employ-machine-learning-to-discover-new-sequences-to-boost-drug-delivery-in-cells-using-neural-networks/

GitHub: https://github.com/learningmatter-mit/peptimizer

Paper: https://www.nature.com/articles/s41557-021-00766-3

Hi everyone,

I'm wondering about diagnostics for Machine Learning models, specifically random forests or gradient boosting.

Often when doing traditional statistics one looks at things like residual normality plots etc in order to assess the validity of their assumptions. However these diagnostics also often steer one in the direction of what is missing from their model.

In the case of things like Random Forests I don't know what to look for, and what to make of what I see. For example I recently trained a model that showed clear bias in the residual (y-yhat against y) plot, but I didn't quite know if this meant what it used to mean to me, and what to do about it.

I know cross-validation etc, but I'm talking about the model building stage, not simply evaluating the performance or tweaking parameters. Yes those things are vitally important, but distinct in my mind.

TLDR: I went into this looking answer 7 questions.

1. Is Gibby powerful on a repeatable and game-breaking way?
2. Does controller actually dominate close-ranged engagements: i(y) is it due to aim assist being overpowered?
3. Are any pro players using any publicly available recoil cheats?
4. How long are optimal engagements?
5. How fast are optimal rotations?
6. Optimal backpack load out
7. Who has the highest ELO, and why?

#ABSTRACT The Kirk Matrix is a repurpose of the football ranking algorithm Colley Matrix. The Bradley–Terry artificial neural network model for individual ratings in group competitions was the conceptual mathematical framework used for assigning rankings. This method is based on simple statistical principles and revolving variables to remain flexible and free of bias.

Findings

#Gibralter

1. Gibraltar has the lowest DPS, lowest individual win rate, highest incoming damage, and lowest TTK. Gibraltar dies first in isolated 3v3 encounters at a 23.1% higher rate than the next highest Bloodhound. Gibraltar is the most likely character to be team shot.
2. Bubble encounters are 50/50 toss ups, and no one team is substantially better than the others. The average bubble fight lasts :48 seconds, and the average result and 4 of 6 people knocked.
3. Gibraltar actually runs a slightly negative value over replacement legend, and Gibraltar mains occupy mostly the bottom ELO quadrant and the empirical data sort of supports that.

#Aim Assist Numbers are base100, the amount of legitimate long distance fights in ALGS were so small they had to be combined with medium ranges into one sub cat.

1. Bubble Encounters MNK 54 - 46 Controller
2. Short Range Encounters MNK 52 - 48 Controller
3. Ranged encounters MNK 51 - 49 Controller
4. Total One Clips slotted by input MNK 38 - 62 Controller
5. The ADS turn speed advantage for MNK allows it to dominate the quite chaotic bubble fights
6. Even with aim assist, controllers are still losing engagements at every distance. The caveat to that is the ability to one clip at a far higher rate. Boom or bust.

#CHEATERS

1. To my surprise, not one ALGS player in NA/EU/ApacN recoil was flagged. I believe this to be the case.

#OPTIMAL ENGAGEMENTS

1. What I discovered was that exchanging knocks had a higher correlation to being third party than time. This is undoubtedly due to the kill feed, the respect to not third party and awareness of who’s were. The findings in this section are completely flawed and s

Machine learning has been widely used in finding efficient solutions in the healthcare sector to help in diagnosing and treating various illnesses.

Sarepta Therapeutics, located in Cambridge, Massachusetts, produced a breakthrough drug in 2016 that directly targets the mutated gene responsible for Duchenne muscular dystrophy (DMD). This rare genetic condition weakens muscles in young boys throughout the body until the heart or lungs fail.

This drug uses Antisense phosphorodiamidate morpholino oligomers (PMO), a huge synthetic molecule that permeates the cell nucleus and modifies the dystrophin gene. This allows for the production of a vital protein that is ordinarily absent in DMD patients. PMO, however, is ineffective at entering cells.

Quick Read: https://www.marktechpost.com/2021/08/20/mit-researchers-employ-machine-learning-to-discover-new-sequences-to-boost-drug-delivery-in-cells-using-neural-networks/

GitHub: https://github.com/learningmatter-mit/peptimizer

Paper: https://www.nature.com/articles/s41557-021-00766-3