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STATS 202: Data Mining in R

This class was created by Brainscape user Chad Milando. Visit their profile to learn more about the creator.

By: Chad Milando
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Decks in this class (16)

Chapter 2 - Statistical Learning
3 important facts about e from y ...,
Define prediction,
Define inference
15  cards
Chapter 3 - Linear Regression
Simple linear regression form coe...,
What is rss tss,
Hypothesis test
18  cards
Chapter 4 - Classification
Logistic Regression, LDA,
15  cards
Chapter 10 - PCA
Pca,
The first principle component,
Pca example
7  cards
Chapter 5 - Cross Validation
Validation set approach,
Loocv,
K fold cv
13  cards
Chapter 10 - Clustering
Clustering overviewa and types we...,
K means,
Properties of k means algorithm 4
6  cards
Missing Data
Types of missing data 3,
Ways to deal with missing data 3,
Explain single imputation overall...
7  cards
Chapter 6 - Model Selection
Define model selection,
Best subset selection,
Alternatives to minimizing cv err...
9  cards
Chapter 6 - Shrinkage Methods
Overview idea why do we want to d...,
Ridge regression definition 2 notes,
The lasso
10  cards
Chapter 6 - Dimensionality Reduction
Dimensionality reduction idea types,
Principal components regression a...,
Relationship between pcr and ridge
8  cards
Chapter 7 - Non Linear Regression
Strategy for non linear regression,
Cubic splines,
Natural cubic spline
11  cards
Chapter 8 - Decision Trees
Cart 6,
How is the tree built,
How do we control for overfitting
9  cards
Chapter 9 - Max Margin + SVC
Hyperplanes and normal vectors 4,
Maximal margin classifier,
Finding maximal margin classifier
8  cards
Chapter 8 - Bagging, Random Forest, Boosting
Bagging what is it when do we do it,
Bagging decision trees,
Out of bag error
6  cards
Chapter 9 - Non-linear Boudary, Kernal, SVM
How do we use the svc with non li...,
Kernels,
The kernel trick
9  cards
Chapter 3 - Problems with Linear Regression
Potential issues in linear regres...,
Potential issue with linear regre...,
Potential issue with linear regre...
9  cards

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STATS 202: Data Mining in R

  • Class purpose General learning

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