INTRODUCTION
Dear reader,
If you lot are a newbie inward the earth of machine learning, in addition to then this tutorial is precisely what you lot withdraw inward guild to innovate yourself to this exciting novel purpose of the information scientific discipline world.
This postal service includes a total machine learning projection that volition conduct you lot measuring past times measuring to do a “template,” which you lot tin forcefulness out exercise afterward on other datasets.
Before proceeding, delight follow our short tutorial.
Look at the examples given in addition to endeavor to empathize the logic behind them. Then endeavor to solve the exercises below using R in addition to without looking at the answers. Then see solutions to cheque your answers.
Look at the examples given in addition to endeavor to empathize the logic behind them. Then endeavor to solve the exercises below using R in addition to without looking at the answers. Then see solutions to cheque your answers.
Exercise 1
Create a variable “x” in addition to attach to it the input attributes of the “iris” dataset. HINT: Use columns 1 to 4.
Exercise 2
Create a variable “y” in addition to attach to it the output attribute of the “iris” dataset. HINT: Use column 5.
Exercise 3
Create a whisker plot (boxplot) for the variable of the start column of the “iris” dataset. HINT: Use
boxplot(). Exercise 4
Now do a whisker plot for each 1 of the 4 input variables of the “iris” dataset inward 1 image. HINT: Use
par(). Learn more about machine learning inward the online course Beginner to Advanced Guide on Machine Learning amongst R Tool. In this course of pedagogy you lot volition larn how to:
- Create a machine learning algorithm from a beginner indicate of view
- Quickly dive into to a greater extent than advanced methods inward an accessible measuring in addition to amongst to a greater extent than explanations
- And much more
Exercise 5
Create a barplot to breakdown your output attribute. HINT: Use plot().
Exercise 6
Create a scatterplot matrix of the “iris” dataset using the “x” in addition to “y” variables. HINT: Use
featurePlot(). Exercise 7
Create a scatterplot matrix amongst ellipses around each separated group. HINT: Use
plot="ellipse". Exercise 8
Create box in addition to whisker plots of each input variable again, merely this fourth dimension broken downward into separated plots for each class. HINT: Use
plot="box". Exercise 9
Create a listing named “scales” that includes the “x” in addition to “y” variables in addition to set
relation to “free” for both of them. HINT: Use list() Exercise 10
Create a density plot matrix for each attribute past times flat value. HINT: Use
featurePlot(). ______________________________________
Below are the solutions to these exercises on visualizing datasets to utilise machine learning.
#################### # # # Exercise 1 # # # #################### install.packages("caret") library(caret) data(iris) validation <- createDataPartition(iris$Species, p=0.80, list=FALSE) validation20 <- iris[-validation,] iris <- iris[validation,] x <- iris[,1:4] #################### # # # Exercise two # # # #################### library(caret) y <- iris[,5] #################### # # # Exercise iii # # # #################### library(caret) boxplot(x[,1], main=names(iris)[1]) #################### # # # Exercise 4 # # # #################### library(caret) par(mfrow=c(1,4)) for(i in 1:4) { boxplot(x[,i], main=names(iris)[i]) } #################### # # # Exercise five # # # #################### library(caret) plot(y) #################### # # # Exercise half dozen # # # #################### library(caret) featurePlot(x=x, y=y) #################### # # # Exercise vii # # # #################### install.packages("ellipse") library(ellipse) library(caret) featurePlot(x=x, y=y,plot="ellipse") #################### # # # Exercise 8 # # # #################### library(caret) featurePlot(x=x, y=y, plot="box") #################### # # # Exercise nine # # # #################### library(caret) scales <- list(x=list(relation="free"), y=list(relation="free")) #################### # # # Exercise 10 # # # #################### library(caret) scales <- list(x=list(relation="free"), y=list(relation="free")) featurePlot(x=x, y=y, plot="density", scales=scales)
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http://www.r-exercises.com/2017/09/08/visualizing-dataset-to-apply-machine-learning-exercises-solutions/
http://www.r-exercises.com/2017/09/08/visualizing-dataset-to-apply-machine-learning-exercises/