Árbol de decisión - Teoría, aplicación y modelado usando R

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Árbol de decisión – Teoría, aplicación y modelado usando R. Analytics (segmentación) objetivo: aprender ciencias de datos (estadística aplicada) CHAID / CART / GINI / ID3 / Random Bosque etc.

40,00  40,00 

No te pierdas este fabuloso curso online llamado Árbol de decisión – Teoría, aplicación y modelado usando R. Es 100% online y comenzarás justo en el momento de matricularte. Tú serás el que marques tu propio ritmo de aprendizaje.

Breve descripción del curso llamado Árbol de decisión – Teoría, aplicación y modelado usando R

Analytics (segmentación) objetivo: aprender ciencias de datos (estadística aplicada) CHAID / CART / GINI / ID3 / Random Bosque etc.

El profesor de este fabuloso curso 100% online es Gopal Prasad Malakar, un auténtico experto en la materia, y con el que aprenderás todo lo necesario para ser más competitivo. El curso se ofrece en Inglés.

Descripción completa del curso llamado Árbol de decisión – Teoría, aplicación y modelado usando R

Course Description What is this course? Decision Tree Model building is one of the most applied technique in analytics vertical. The decision tree model is quick to develop and easy to understand. The technique is simple to learn. A number of business scenarios in lending business / telecom / automobile etc. require decision tree model building. This course ensures that student get understanding of what is the decision tree where do you apply decision tree what benefit it brings what are various algorithm behind decision tree what are the steps to develop decision tree in R how to interpret the decision tree output of R Course Tags Decision Tree CHAID CART Objective segmentation Predictive analytics ID3 GINI Material in this course the videos are in HD format the presentation used to create video are available to download in PDF format the excel files used is available to download the R program used is also available to download How long the course should take? It should take approximately 8 hours to internalize the concepts and become comfortable with the decision tree modeling using R The structure of the course Section 1 – motivation and basic understanding Understand the business scenario, where decision tree for categorical outcome is required See a sample decision tree – output Understand the gains obtained from the decision tree Understand how it is different from logistic regression based scoring Section 2 – practical (for categorical output) Install R – process Install R studio – process Little understanding of R studio /Package / library Develop a decision tree in R Delve into the output Section 3 – Algorithm behind decision tree GINI Index of a node GINI Index of a split Variable and split point selection procedure Implementing CART Decision tree development and validation in data mining scenario Auto pruning technique Understand R procedure for auto pruning Understand difference between CHAID and CART Understand the CART for numeric outcome Interpret the R-square meaning associated with CART Section 4 – Other algorithm for decision tree ID3 Entropy of a node Entropy of a split Random Forest Method Why take this course? Take this course to Become crystal clear with decision tree modeling Become comfortable with decision tree development using R Hands on with R package output Understand the practical usage of decision tree