Hertha  Walsh

Hertha Walsh

1601723940

XGBoost, LightGBM, and Other Kaggle Competition Favorites

Kaggle is the data scientist’s go-to place for datasets, discussions, and perhaps most famously, competitions with prizes of tens of thousands of dollars to build the best model.

With all the flurried research and hype around deep learning, one would expect neural network solutions to dominate the leaderboards. It turns out, however, that neural networks — while indeed very powerful algorithms — have very limited applications, being useful really only in image recognition, language modelling, and occasionally sequence prediction.

Instead, top winners of Kaggle competitions routinely use gradient boosting. It’s worth looking at the intuition of this fascinating algorithm and why it has become so popular among Kaggle winners.

Decision trees are relatively weak on their own — predictions are formed based solely on yes/no questions. The feature space is split into hypercubes, which may not be appropriate for many datasets that cannot be separated by vertical and horizontal planes. However, ensembles of trees that combine learned insights from different models can be very powerful.

Traditional ensemble models — the Random Forest algorithm — form ensembles by training several trees on random subsets of the data. This allows different trees to ‘specialize’ on certain parts of the data while considering the inputs of other trees when making a prediction on an input.

Random Forest can be effective, but it is expensive to train because trees are added completely at random. There is no clear difference between training twenty trees and fifteen trees, and the objective of each tree is to optimize its own performance on its data subset, not necessarily the performance of the ensemble as a whole.

#data-science #deep-learning #artificial-intelligence #machine-learning #ai

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Buddha Community

XGBoost, LightGBM, and Other Kaggle Competition Favorites
Vern  Greenholt

Vern Greenholt

1598236620

Kaggle Beginner Competitions Can Be Cheated

The purpose of this article is to warn new kagglers before they waste their time on trying to get an impossible score. Some kagglers got maximum accuracy with one click. Before we discuss how they did it and why — let’s introduce shortly Kaggle scoring model to understand why would even somebody try to cheat.

Kaggle Progression System

Kaggle is a portal where data scientists, machine learning experts, and analytics can challenge their skills, share knowledge, and take part in various competitions. And it is open to every level of experience — from complete newbie to grandmaster. You can use open datasets to broaden your knowledge, gain kudos/swag, and even win money.

Image for post

Some of the available competitions. (Image by author)

Winning competitons, taking part in discusions, and sharing your ideas result in medals. Medals are presented on your profile along with all your achievements.

Image for post

#data-science #beginner #kaggle-competition #competition #kaggle #data science

Kaggle Beginner Competitions Can Be Cheated

The purpose of this article is to warn new kagglers before they waste their time on trying to get an impossible score. Some kagglers got maximum accuracy with one click. Before we discuss how they did it and why — let’s introduce shortly Kaggle scoring model to understand why would even somebody try to cheat.

Kaggle Progression System

Kaggle is a portal where data scientists, machine learning experts, and analytics can challenge their skills, share knowledge, and take part in various competitions. And it is open to every level of experience — from complete newbie to grandmaster. You can use open datasets to broaden your knowledge, gain kudos/swag, and even win money.

Image for post

Some of the available competitions. (Image by author)

Winning competitons, taking part in discusions, and sharing your ideas result in medals. Medals are presented on your profile along with all your achievements.

#data-science #beginner #kaggle-competition #competition #kaggle #data science

Kawsar  Ahmed

Kawsar Ahmed

1612457100

Tuning Model Hyper-Parameters for XGBoost and Kaggle

Properly setting the parameters for XGBoost can give increased model accuracy/performance. This is a very important technique for both Kaggle competitions and data science in general. In this video I will show how I automated one popular technique for XGBoost.

Code is here: https://github.com/jeffheaton/jh-kaggle-util

Subscribe: https://www.youtube.com/channel/UCR1-GEpyOPzT2AO4D_eifdw

#kaggle #xgboost #data-science

Lessons From My First Kaggle Competition

How I chose my first Kaggle competition to enter and what I learned from doing it.

A little background

I find starting out in a new area of programming a somewhat daunting experience. I have been programming for the past 8 years, but only recently have developed a keen interest in Data Science. I want to share my experience to encourage you to take the plunge too!

I started out dipping my toe in the ocean of this vast topic with a couple of the Kaggle mini-courses. I didn’t need to learn how to write in Python, but I needed to equip myself with the tools to do the programming that I wanted. First up was Intro to Machine Learning — it seemed like a good place to start. As part of this course you contribute to an in course competition, but even after completing it, I didn’t feel prepared to do a public competition. Cue Intermediate Machine Learning, where I learned to use a new model and how to think deeper about a data problem.

#2020 sep tutorials # overviews #competition #data science #kaggle

Wanda  Huel

Wanda Huel

1602925200

XGBoost — Queens of Boosting Algorithms?

XGBoost is well known for its faster-execution and Scalability, mainly designed for Speed and Performance. Today XGBoost has become a de-facto algorithm for winning competitions at Kaggle. Similar to all other boosting algorithms, XGBoost also mainly focuses on reducing the error.

XGBoost, a scalable tree boosting system that is widely used by data scientists and provides state-of-the-art results on many problems.

Basic Working of XGBoost Algorithm

  1. Building base-models and making predictions on the given data.

  2. Calculating the Error and set this _error _as target.

  3. Building model on errors and make predictions

  4. Updating the _predictions _of the previous model.

  5. Repeat the above steps 2 to 4.

#machine-learning #data-science #boosting #kaggle-competition #xgboost