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2019

09

23

CS301_Proj3

CS301_Proj3DescriptionThe City of Madison has many different agencies providing a variety of services. In this project,

2019-09-23 CS301Proj

CS301 Data Programming I

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ISLR_ch3.3 Potential Problems

ISLR_ch3.3 Potential Problems

ISLR_ch3.3 Potential ProblemsQualitative PredictorsPredictors with Only Two LevelsSuppose that we wish to investigate di

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch2.2 Assessing Model Accuracy

ISLR_ch2.2 Assessing Model Accuracy

ISLR_ch2.2 Assessing Model Accuracyno free lunch in statistics: no one method dominates all others over all possible dat

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch3.2 Multiple Linear Regression

ISLR_ch3.2 Multiple Linear Regression

ISLR_ch3.2 Multiple Linear Regressionmultiple linear regression model takes the form: \begin{align} Y=\beta_0+\beta_1X_1

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch4.3 Logistic_Regression

ISLR_ch4.3 Logistic_Regression

ISLR_ch4.3 Logistic_RegressionWhy Not Linear Regression?Linear regression is not appropriate in the case of a qualitativ

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch3.5 Comparison_of_Linear_Regression_with_K-Nearest_Neighbors

ISLR_ch3.5 Comparison_of_Linear_Regression_with_K-Nearest_Neighbors

ISLR_ch3.5 Comparison_of_Linear_Regression_with_K-Nearest_NeighborsParametric v.s. Non-parametricLinear regression is an

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch4.4 Linear_Discriminant_Analysis

ISLR_ch4.4 Linear_Discriminant_Analysis

ISLR_ch4.4 Linear_Discriminant_AnalysisLDA V.S. Logistic Regression: When the classes are well-separated, the parameter

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch4.5 Comparison_of_Classification_Methods

ISLR_ch4.5 Comparison_of_Classification_Methods

ISLR_ch4.5 Comparison_of_Classification_MethodsComparing Logistic Regression, LDA, QDA, and KNNlogistic regression and L

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch6.0 Intro_Model_Selection

ISLR_ch6.0 Intro_Model_Selection

ISLR_ch6.0 Intro_Model_SelectionSetting: In the regression setting, the standard linear model $Y = β_0 + β_1X_1 + · · ·

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch5.1 Cross_Validation

ISLR_ch5.1 Cross_Validation

ISLR_ch5.1 Cross_ValidationResampling methods:involve repeatedly drawing samples from a training set and refitting a mod

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch6.1 Subset_Selection

ISLR_ch6.1 Subset_Selection

ISLR_ch6.1 Subset_SelectionIntro to model selectionSetting: In the regression setting, the standard linear model $Y = β

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch5.2 Potential Problems

ISLR_ch5.2 Potential Problems

ISLR_ch5.2 Potential ProblemsApproach: A data set, which we call Z, that contains n observations. We randomly select n

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch6.2 Shrinkage Methods

ISLR_ch6.2 Shrinkage Methods

ISLR_ch6.2 Shrinkage MethodsShrinkage Methods v.s. Subset Selection: Subset selection methods described involve using l

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch6.4 Considerations_In_High_Dimensions

ISLR_ch6.4 Considerations_In_High_Dimensions

ISLR_ch6.4 Considerations_In_High_DimensionsHigh-Dimensional DataHigh-dimensional: Data sets containing more features th

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch6.3 Dimension_Reduction-PCA

ISLR_ch6.3 Dimension_Reduction-PCA

ISLR_ch6.3 Dimension_Reduction-PCAIntro to Dimension Reduction MethodsSubset selection and shrinkage methods all use the

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch8.1 The_Basics_of_Decision_Trees

ISLR_ch8.1 The_Basics_of_Decision_Trees

ISLR_ch8.1 The_Basics_of_Decision_TreesRegression TreesPredicting Baseball Players’ Salaries Using Regression Trees Te

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch8.2 Bagging,Random_Forest,Boosting

ISLR_ch8.2 Bagging,Random_Forest,Boosting

ISLR_ch8.2 Bagging,Random_Forest,BoostingBaggingBootstrap aggregation, or bagging, is a general-purpose procedure for re

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch9.1 Maximal_Margin_Classifier

ISLR_ch9.1 Maximal_Margin_Classifier

ISLR_ch9.1 Maximal_Margin_ClassifierWhat Is a Hyperplane?In a p-dimensional space, a hyperplane is a flat affine subspac

2019-09-23 An Introduction to statistical Learning

Machine Learning notes

23

ISLR_ch3.1 Simple Linear Regression

ISLR_ch3.1 Simple Linear Regression

ISLR_ch3.1 Simple Linear RegressionSimple linear regression: approach for predicting a quantitative response Y on the ba

2019-09-23 An Introduction to statistical Learning

Machine Learning notes