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HW09Review of Major Topics

Q1 – Classifier Performance Comparison

Q1a –Analyze the data set Social_Network_Ads.csv and create the plot with correct titles on axes:

Q1b Use the following classifiers

• Naïve Bayes
• Logistic Regression
• Decision Trees
• KNN
• Support Vector Machine
• Random Forest

For each classifier show

• The classifier boundary for training and test
• Printout your 1^st name on all graphs

Q1c Compare the confusion matrix in the following table for the above data set

Q2 – Principal Component Analysis

Summarize how the PCA algorithm works using the following link and recreate the code for the IRIS data set.

https://plot.ly/ipython-notebooks/principal-component-analysis/

Q3 Review the material on PCA in the following and visually describe how PCA works (use snapshots)

http://setosa.io/ev/principal-component-analysis/

Q4LDA Explain how LDA differs from PCA

https://sebastianraschka.com/Articles/2014_python_lda.html

Q5 Compare accuracy of LDA vs PCA techniques using the dimensionality reduction on Wine data.