BEFORE DOING YOUR HOMEWORK, EXAMINE THE FOLLOWING NODES AND EXAMPLES.

Node repository

$1\mathrm{.}1.$ Mining

$1\mathrm{.}1\mathrm{.}1.$ Decision Tree

$1\mathrm{.}1\mathrm{.}1\mathrm{.}1.$ Decision tree learner

$1\mathrm{.}1\mathrm{.}1\mathrm{.}2.$ Decision tree predictor

$1\mathrm{.}1\mathrm{.}1\mathrm{.}3.$ Decision tree to Image

$1\mathrm{.}1\mathrm{.}1\mathrm{.}4.$ Simple regression learner

$1\mathrm{.}1\mathrm{.}1\mathrm{.}5.$ Simple regression predictor

$1\mathrm{.}1\mathrm{.}2.$ Decision Tree Ensemble

$1\mathrm{.}1\mathrm{.}2\mathrm{.}1.$ Classification

$1\mathrm{.}1\mathrm{.}2\mathrm{.}1\mathrm{.}1.$ Gradient boosted trees learner

$1\mathrm{.}1\mathrm{.}2\mathrm{.}1\mathrm{.}2.$ Gradient boosted trees predictor

$1\mathrm{.}1\mathrm{.}2\mathrm{.}2.$ Regression

$1\mathrm{.}1\mathrm{.}2\mathrm{.}2\mathrm{.}1.$ Gradient boosted trees learner $($Regression$)$

$1\mathrm{.}1\mathrm{.}2\mathrm{.}2\mathrm{.}2.$ Gradient boosted trees predictor $($Regression$)$

$1\mathrm{.}1\mathrm{.}2\mathrm{.}3.$ Random forest

$1\mathrm{.}1\mathrm{.}2\mathrm{.}3\mathrm{.}1.$ Classification

$1\mathrm{.}1\mathrm{.}2\mathrm{.}3\mathrm{.}1\mathrm{.}1.$ Random forest learner

$1\mathrm{.}1\mathrm{.}2\mathrm{.}3\mathrm{.}1\mathrm{.}2.$ Random forest Predictor

$1\mathrm{.}1\mathrm{.}2\mathrm{.}3\mathrm{.}2.$ Regression

$1\mathrm{.}1\mathrm{.}2\mathrm{.}3\mathrm{.}2\mathrm{.}1.$ Random forest learner $($Regression$)$

$1\mathrm{.}1\mathrm{.}2\mathrm{.}3\mathrm{.}2\mathrm{.}2.$ Random forest learner $($Regression$)$

EXAMPLES $-$ Analytics Section

$2\mathrm{.}1.04\_$Classifications$\_$and$\_$Predictive$\_$Modeling

$2\mathrm{.}1\mathrm{.}1.01$ Example for Learning a Decision Tree

$2\mathrm{.}1\mathrm{.}2.05$ Gradient Boosted Trees

$2\mathrm{.}2.$ Regression

$2\mathrm{.}2\mathrm{.}1.$ Learning a Simple Regression Tree

Writing Report

Write a report about the following $3$ classification examples from Knime Hub. Provide brief

information about Input Data for each example. Briefly describe each used node. Lastly,

interpret the results of each example. In reporting, also use visualization techniques.

Placement of classification examples in Knime Hub is given below:

Analytics

$04$ Classifications and Predictive Modelling

$04$ Exporting a Decision Tree as Image $($Example$-1)$

$07$ Decision Tree $($Example$-2)$

$09$ Random Forest $($Example$-3)$