XGBoost MLM Flashcards

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1
Q

XGBoost, short for eXtreme Gradient Boosting

A

XGBoost, short for eXtreme Gradient Boosting, is an optimized distributed gradient boosting library that provides a highly efficient, flexible, and portable solution for machine learning tasks.

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2
Q
  1. Introduction
A

XGBoost is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. It is renowned for its execution speed and model performance, and it has been a go-to choice for many winning teams of machine learning competitions.

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3
Q
  1. Gradient Boosting
A

Gradient Boosting is a technique that produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion and it generalizes them by allowing optimization of an arbitrary differentiable loss function.

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4
Q
  1. Speed and Performance
A

XGBoost is recognized for its speed and performance. The core XGBoost algorithm is parallelizable, which means it can harness all of the processing power of modern multi-core computers. Furthermore, it is also capable of being distributed across networks of computers to handle larger datasets.

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5
Q
  1. Regularization
A

XGBoost has an in-built regularization which helps to reduce overfitting. In fact, XGBoost is also known as a ‘regularized boosting’ technique.

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6
Q
  1. Handling Missing Values
A

XGBoost has an in-built routine to handle missing values, which allows the user to choose a different split for handling missing values, and uses this to learn the best imputation value for missing values based on reduction in the loss function.

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7
Q
  1. Tree Pruning
A

XGBoost uses a more principled approach for controlling model complexity and preventing overfitting through its depth-first tree pruning strategy, where splits are chosen to optimize for the loss function and a specified maximum depth, while traditional gradient boosting methods use a greedy algorithm.

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8
Q
  1. Built-in Cross-Validation
A

XGBoost allows a user to run a cross-validation at each iteration of the boosting process, making it easy to get the exact optimum number of boosting iterations in a single run.

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9
Q
  1. Flexibility
A

XGBoost allows users to define custom optimization objectives and evaluation criteria, which adds a whole new dimension to the model as now you can solve for almost all types of problems.

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10
Q
  1. Strengths and Limitations
A

XGBoost performs well in many predictive tasks and is often a key component of winning entries in machine learning competitions. However, due to its complexity and flexibility, it requires careful tuning of the hyperparameters for best performance.

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11
Q
  1. Applications
A

XGBoost has been used successfully in many machine learning and data science competitions and has a wide range of applications in industries such as banking, e-commerce, and healthcare.

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