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Hello,
Please answer the following assignments in separate documents as per the instructions with code line by line explaining
Week 6 #1: Using Decision Trees
Use the descision tree models from this chapter to make predictions using the iris datasets. Compare the results to what you have achieved previously. Show and comment your code line by line so that the instructor knows that you know what each line of code is doing.
Along with an R Markdown file, submit a Word document with screenshots of your code with comments for each image. Include a timestamp and a sliver of your desktop.
Comment your code line by line such that the reader will know that you know what each function and each function parameter is doing.
Points will be deducted in case you fail to explain the output. ==============================================================
Week 6 #2: DNN
Use DNN to analyze the Iris dataset. Compare your results. Show your code commented line by line and provide a narrative comparison to the results from other models.
Along with the R Markdown file, submit a Word document with screenshots of your code with comments for each image. Include a timestamp and a sliver of your desktop.
Comment your code line by line such that the reader will know that you know what each function and each function parameter is doing.
Points will be deducted in case you fail to explain the output.
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Overview
Overview
Attached Files:
DNNs.pdf DNNs.pdf – Alternative Formats (285.598 KB)
IrisExercise.pdf IrisExercise.pdf – Alternative Formats (507.681 KB)
IrisDataaset.pptx IrisDataaset.pptx – Alternative Formats (468.185 KB)
ML_for_the_birds.pdf ML_for_the_birds.pdf – Alternative Formats (916.811 KB)
Classic MLWe’ll use R to analyze a dataset of Iris flower measurements. This analysis was first performed in 1936 by Ronald Fisher
The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other.
We try six different models: LDA, CART, kNN, SVM, RF, and SQA.
LDA is the most accurate.
Bleeding Edge MLWe repeat our analysis of the Iris Dataset with a DNN (Deep Neural Network).
DNNs are very useful for:Large high-dimensional data sets.
They require MASSIVE amounts of CPU time to train.
They give good results with very little human effort.
They are often used for facial recognition and natural language processing.
Intro to “Deep Learning”
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NB: We do not resell papers. Upon ordering, we do an original paper exclusively for you.
