STATS 216V
Homework 4 Problem 1
(a) Plot of test MSE for Bagging and Random Forest
(b) Importance of features
Yu Zhang
STATS 216V
Homework 4 Problem 1
Yu Zhang
Two measures of variable importance are reported. The former %IncMSE is based upon the
mean dec
Stats216: Session 8
Predicting ALS Disease Progression
ALS (amyotrophic lateral sclerosis), or Lou Gehrigs disease, is a fatal neurodegenerative disease with no
known cure and few known causes. In July of 2012, Prize4Life launched a challenge to most accu
STATS216v Introduction to Statistical Learning
Stanford University, Summer 2016
Problem Set 2
Due: July 15
Remember the university honor code. All work and answers must be your own.
Problem 1
Suppose we collect data for a group of students in a statistics
STATS 216 Introduction to Statistical Learning
Stanford University, Summer 2015
Problem Set 1 Solutions
Total: 65 points
1. 15 points: (a) 6 points (b) 6 points (c) 3 points
Grading notes: For parts (a) and (b), each example is worth 2 points: 1 point for
STATS 216 Introduction to Statistical Learning
Stanford University, Summer 2015
Problem Set 3 Solutions
Total: 65 points
1. 14 points: one point for each correct answer (a, b, and c i-v) and one point for each
explanation that correctly explains either th
STATS216v Introduction to Statistical Learning
Stanford University, Summer 2016
Problem Set 2 (Solutions)
Total: 100 points
Problem 1
10 points. Grading notes: 5 points for each part. Identifying the correct formula is worth
3 points, and the correct form
STATS 216 Introduction to Statistical Learning
Stanford University, Winter 2016
Problem Set 1 Solutions
Total: 68(+1) points
1. 15 points: (a) 3 points (b) 6 points (c) 6 points
Grading notes: For parts (b) and (c), each example is worth 2 points: 1 point
STATS 216 Introduction to Statistical Learning
Stanford University, Winter 2015
Problem Set 2
Due: Wednesday, February 10, 2016
Remember the university honor code. All work and answers must be your own.
1. Suppose we collect data for a group of students i
STATSZ 16V
(3)
(bl
Homework 3 — Problem 1 Yu Zhang
Best subset selection has the smallest training RSS.
Because Best subset selection considers all the possible combinations of models, while the rest
of the two models use greedy approach.
All of the three
STATS216v Introduction to Statistical Learning
Stanford University, Summer 2015
Problem Set 1
Due: Friday, July 3
Remember the university honor code. All work and answers must be your own.
1. In this question we consider some real-life applications of sta
STATS 216 Introduction to Statistical Learning
Stanford University, Summer 2015
Problem Set 2 Solutions
Total: 65 points
1. 15 points
Grading notes: Each part is worth 3 points, 2 for the correct answer and 1 for a valid
explanation. Student explanations
STATS216v Introduction to Statistical Learning
Stanford University, Summer 2015
Problem Set 2
Due: Friday, July 17
Remember the university honor code. All work and answers must be your own.
1. Suppose we estimate the regression coecients in a linear regre
STATS 216 Introduction to Statistical Learning
Stanford University, Winter 2015
Problem Set 2 Solutions
Total: 70 points
1. Add me 4 points
Grading notes: 2 points for each part. Identifying the correct formula is worth 1 point,
and the correct formula pl
STATS216v Introduction to Statistical Learning
Stanford University, Summer 2016
Problem Set 1
Due: July 1
Remember the university honor code. All work and answers must be your own.
Problem 1
Explain whether each scenario below is a regression, classificat
STATS216v Introduction to Statistical Learning
Stanford University, Summer 2015
Problem Set 3
Due: Friday, July 31
Remember the university honor code. All work and answers must be your own.
1. We do best, forward, and backward stepwise selection on a sing
STATS216v Introduction to Statistical Learning
Stanford University, Summer 2015
Problem Set 4
Due: Wednesday, August 12
Remember the university honor code. All work and answers must be your own.
1. Recall the body dataset from problem 3 of Homework 3. In
Prchlern 1.
(e)
il-
ii
iii,
ii,
iii,
ii,
iii
STATSQlﬁ: Problem Set 1
[Ruixi Lin] — [r1in2]
Jenner}; 25, 2016
An eccncniist wents tc predict the price per squere fcct hesed
en the hcusing erees. This is e regressicn rncdel where hcusing
eree is the predict
Stats216: Session 2
Linear Regression Analysis of NCAA Basketball Data
In this in-class session, we will analyze a data set containing the outcomes of every game in the 2012-2013
regular season, and the postseason NCAA tournament. There are 5541 games and
Stats216: Session 2
Linear Regression Analysis of NCAA Basketball Data
In this in-class session, we will analyze a data set containing the outcomes of every game in the 2012-2013
regular season, and the postseason NCAA tournament. There are 5541 games and
Vectorization, Timing, and Parallelization
Stats 216
27 January 2016
In this class were going to take a step back from statistical analysis and instead take a closer look at whats
going on under the hood when you run a program in R. This is incredibly imp
Stats216: Session 2
Linear Regression Analysis of NCAA Basketball Data
In this in-class session, we will analyze a data set containing the outcomes of every game in the 2012-2013
regular season, and the postseason NCAA tournament. There are 5541 games and
Regression and Simulation
This is an introductory R session, so it may go slowly if you have never used R before. Do not be discouraged.
A great way to learn a new language like this is to plunge right in.
We will simulate some data suitable for a regress
Regression and Simulation
This is an introductory R session, so it may go slowly if you have never used R before. Do not be discouraged.
A great way to learn a new language like this is to plunge right in.
We will simulate some data suitable for a regress
Stats216: Session 2
Linear Regression Analysis of NCAA Basketball Data
In this in-class session, we will analyze a data set containing the outcomes of every game in the 2012-2013
regular season, and the postseason NCAA tournament. There are 5541 games and
STATS 216 Introduction to Statistical Learning
Stanford University, Winter 2016
Problem Set 3
Due: Wednesday, February 24, 2016
Remember the university honor code. All work and answers must be your own.
1. Consider two curves, g1 and g2 , defined by
!
Z
n
Vectorization, Timing, and Parallelization Solutions
Stats 216
27 January 2016
In this class were going to take a step back from statistical analysis and instead take a closer look at whats
going on under the hood when you run a program in R. This is incr