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Machine learning -- Databricks Documentation

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XGBoost is a popular machine learning library designed specifically for training decision trees and random forests. For information about installing XGBoost on Databricks Runtime, or installing a custom version on Databricks Runtime ML, see these instructions. You can train XGBoost models on an individual machine or in a distributed fashion.


Random Forest Vs XGBoost Tree Based Algorithms

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In machine learning, we mainly deal with two kinds of problems that are classification and regression. There are several different types of algorithms for both tasks. But we need to pick that algorithm whose performance is good on the respective data. Ensemble methods like Random Forest, Decision Tree, XGboost algorithms have shown very good results when we talk about classification. These algorithms give high accuracy at fast speed.


Introduction to Boosted Trees -- xgboost 0.6 documentation

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We need to find a way to find the best parameters given the training data. In order to do so, we need to define a so-called objective function, to measure the performance of the model given a certain set of parameters. A very important fact about objective functions is they must always contain two parts: training loss and regularization. The training loss measures how predictive our model is on training data. For example, a commonly used training loss is mean squared error.


How to use XGBoost algorithm in R in easy steps

#artificialintelligence

Did you know using XGBoost algorithm is one of the popular winning recipe of data science competitions? So, what makes it more powerful than a traditional Random Forest or Neural Network? In the last few years, predictive modeling has become much faster and accurate. I remember spending long hours on feature engineering for improving model by few decimals. A lot of that difficult work, can now be done by using better algorithms.


How to use XGBoost algorithm in R in easy steps

#artificialintelligence

Did you know using XGBoost algorithm is one of the popular winning recipe of data science competitions? So, what makes it more powerful than a traditional Random Forest or Neural Network? In the last few years, predictive modeling has become much faster and accurate. I remember spending long hours on feature engineering for improving model by few decimals. A lot of that difficult work, can now be done by using better algorithms.