Docsity
Log inSign up
Docsity
Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity

Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan

Guidelines and tips
Guidelines and tips
Sell on Docsity
Sell on Docsity
Log inSign up

Prepare for your exams

Study with the several resources on Docsity

Find documents

Prepare for your exams with the study notes shared by other students like you on Docsity

Search Store documents

The best documents sold by students who completed their studies

Search through all study resources
Docsity AINEW

Summarize your documents, ask them questions, convert them into quizzes and concept maps

Explore questions

Clear up your doubts by reading the answers to questions asked by your fellow students

Earn points to download

Earn points by helping other students or get them with a premium plan

Share documents
download point icon20 Points

For each uploaded document

Answer questions
download point icon5 Points

For each given answer (max 1 per day)

All the ways to get free points
Get points immediately

Choose a premium plan with all the points you need

Study Opportunities

Choose your next study program

Get in touch with the best universities in the world. Search through thousands of universities and official partners

Community

Ask the community

Ask the community for help and clear up your study doubts

University Rankings

Discover the best universities in your country according to Docsity users

Free resources

Our save-the-student-ebooks!

Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors

From our blog

Exams and Study
Go to the blog

Comparison of ML Algorithms: Naive Bayes, Logistic Regression, Perceptron, LDA, Decision T, Exams of Computer Science

University of California-Santa CruzComputer Science

A comparison of various machine learning algorithms, including naive bayes, logistic regression, perceptron, lda, decision trees, and nearest neighbor. The comparison covers their flexibility, robustness, and computational efficiency. Topics include interpretability, handling of missing values and noise/outliers, irrelevance of features, and monotone transformations. The document also discusses the use of trees and neural networks, and provides a summary of each model.

Typology: Exams

Pre 2010

Uploaded on 09/17/2009

koofers-user-fju-1
koofers-user-fju-1 🇺🇸

10 documents

1 / 1

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
1
Comparison
Some-
what
YesYesYesinterpret
able
mixedNumericNumeric
numeric
Data
Fair/poorOkFatalBadOutliers
yesNoNoyesMissing
values
P(x|y)P(y|x)LTUP(x|y)Models
Naïve
Bayes
Logistic
regression
Perceptron
LDA
Robustness
someBadBadBad
irrelevant
features
goodgoodgoodgood
Compute
time
maybenono
noMonotone
transform
Naïve
Bayes
Logistic
regression
Perceptron
LDA
Tree and NN comparison
Training set no,
but ok for test points
TricksMissing values
Good with knnGood with
pruning
Noise/outliers
Only in 1 or 2
dimensions
If small treeinterpretable
Usually NumericmixedData
Instance based,
flexible
Trees -
flexible
Model
Nearest NeighborDecision
Trees
Tree and KNN Robustness
Lazy -
expensive
OKComputation
time
Very badFairIrrelevant
features
Very badGreat
Monotone
transformation
Nearest
neighbor
Decision tree
Neural Net Summary
• Model: somewhat flexible, ve ry flexible over all
topologies, but must pick topology ,
• Data: Numeric
• Interpretable? NO (but some pretty pictures)
• Missing values? NO
• Noise/outliers? Very good
• Irrelevant features? N o
• Comp. efficiency? Good

Partial preview of the text

Download Comparison of ML Algorithms: Naive Bayes, Logistic Regression, Perceptron, LDA, Decision T and more Exams Computer Science in PDF only on Docsity!

Comparison

Some- what interpret Yes Yes Yes able Data numeric Numeric Numeric mixed Outliers Bad Fatal Ok Fair/poor Missing yes No No yes values Models P(x|y) LTU P(y|x) P(x|y) Naïve Bayes Logistic regression LDA^ Perceptron

Robustness

irrelevant Bad Bad Bad some features Compute good good good good time Monotone no no no maybe transform Naïve Bayes Logistic regression LDA^ Perceptron

Tree and NN comparison

Training set no, but ok for test points Missing values Tricks Good with Good with knn pruning Noise/outliers Only in 1 or 2 dimensions interpretable If small tree Data mixed Usually Numeric Instance based, flexible Trees - flexible Model Decision Nearest Neighbor Trees

Tree and KNN Robustness

Lazy - expensive Computation OK time Irrelevant Fair Very bad features Monotone Great Very bad transformation Nearest neighbor Decision tree

Neural Net Summary

  • Model: somewhat flexible, very flexible over all topologies, but must pick topology,
  • Data: Numeric
  • Interpretable? NO (but some pretty pictures)
  • Missing values? NO
  • Noise/outliers? Very good
  • Irrelevant features? No
  • Comp. efficiency? Good