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Learn to build predictive models with machine learning, using different Rstudio´s packages: ROCR, caret, XGBoost, rparty

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Decision Trees, Random Forests & Gradient Boosting in R

 

 

What you may analyze

 

The set of rules behind recursive partitioning choice timber
Construct conditional inference decision trees with R`s ctree characteristic
Assemble recursive partitioning decision bushes with R`s rpart feature
Learn to estimate Gini´s impurity
Construct ROC and estimate AUC
Random Forests with R´s randomForest bundle
Gradient Boosting with R´s XGBoost package deal
Deal with missing information

 

Necessities

The course includes an creation to the choice timber set of rules so the handiest requirement for the route is a simple expertise of spreadsheets and R. I hope you’re geared up to upgrade your self and learn how to optimize investment portfolios with excel and R.

 

Description

 

Might you like to build predictive models the use of device gaining knowledge of? That´s precisely what you may analyze on this course “decision timber, Random Forests and Gradient Boosting in R.” My name is Carlos Martínez, i’ve a Ph.D.

 

In management from the university of St. Gallen in Switzerland. I’ve offered my research at a number of the most prestigious academic conferences and doctoral colloquiums on the college of Tel Aviv, Politecnico di Milano, university of Halmstad, and MIT. Furthermore, i’ve co-authored greater than 25 teaching instances, some of them protected within the case bases of Harvard and Michigan.

 

 

That is a totally complete path that includes presentations, tutorials, and assignments. The path has a sensible approach primarily based on the studying-by-doing approach in which you’ll examine decision trees and ensemble strategies primarily based on selection trees using a actual dataset. Similarly to the films, you may have get admission to to all of the Excel files and R codes that we can broaden within the videos and to the solutions of the assignments covered in the course with which you’ll self-examine and gain self assurance on your new abilities.

 

 

After a quick theoretical creation, we are able to illustrate little by little the algorithm at the back of the recursive partitioning decision trees. Once we know this set of rules in-depth, we will have earned the proper to automate it in R, using the ctree and rpart functions to respectively construct conditional inference and recursive partitioning selection timber.

 

Furthermore, we can learn to estimate the complexity parameter and to prune bushes to growth the accuracy and decrease the overfitting of our predictive models. After building the choice trees in R, we will also study two ensemble techniques based totally on decision bushes, along with Random Forests and Gradient Boosting.

 

 

Subsequently, we will assemble the ROC curve and calculate the area below such curve, if you want to serve as a metric to examine the goodness of our models.

The best college students of this route are university college students and professionals interested in machine mastering and enterprise intelligence. The path includes an creation to the choice trees algorithm so the most effective requirement for the path is a fundamental understanding of spreadsheets and R.

 

 

I’m hoping you’re prepared to improve yourself and discover ways to optimize funding portfolios with excel and R. I´ll see you in magnificence!

Who this direction is for:
The ideal college students of this path are college students and specialists interested in system getting to know and enterprise intelligence.

Learn to build predictive models with machine learning, using different Rstudio´s packages: ROCR, caret, XGBoost, rparty

Decision Trees, Random Forests & Gradient R


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