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• Confidence interval for Logistic Regression
• Confidence interval for step functions

https://www.statlearning.com/resources-second-edition

Use sklearn.preprocessing.StandardScaler to scale training set features and apply same transform to the test set.

I've also transcribed the Statistical Learning material into Python but only for my own reference. Yours is very elegant and I wish I'd discovered it at the beginning of the course rather than the end.

I noticed that you've gone to the extra step of dumping the R data to file and then loading into the Python environment in Jupyter. In my notes I wanted to have the R and Python code on top of each other for easy reference, so I installed an R virtualenv and built a "bilingual" Jupyter kernel that handles both languages. So far I've had good results using this version of rpy2 with Python 3.5 to invoke R magics in Jupyter and intersperse the two languages in the same notebook.

See my attached notebook, in case you find it useful.
StatLearning_Chapter4R_inPython.ipynb.zip

Library not loaded: /usr/local/opt/gcc/lib/gcc/9/libgfortran.5.dylib
Reason: image not found

Hi,
I am trying to run a code same as for chapter 6 provided here:

When I reach the point:
In[7]:

     grid = 10**np.linspace(10,-2,100)

     ridge3 = gln.ElasticNet(alpha=0, lambda_path=grid)
     ridge3.fit(X, y)

I get this error:

AttributeError Traceback (most recent call last)
in ()
1 grid = 10**np.linspace(10,-2,100)
2
----> 3 ridge3 = gln.ElasticNet(alpha=0, lambda_path=grid)
4 ridge3.fit(X, y)

AttributeError: module 'glmnet' has no attribute 'ElasticNet'

What can I do about it?

I moved my local repository to another environment and need to fix the graphviz/pydot setup to be able to create the graphical representation of the decision trees. I accidently pushed an update to GitHub.

• Chapter 3
• Chapter 4
• Chapter 5
• Chapter 6
• Chapter 7
• Chapter 8
• Chapter 9
• Chapter 10

What seems to be the problem with this plot? I think I created the meshgrid correctly, but it does not have the symmetrical shape as the one in the book.

Python

ISL

I contacted Trevor Hastie to ask him about the R code for plot on the left. He was so kind to send me the following code. You will need to load the advertising data first and skip the first two lines and the last line.

load("Chapter3.RData")
postscript(file="../Figs/leastsqexample1.ps",width=7,height=7,pointsize=14,horizontal=F)
set.seed(22)
par(mfrow=c(1,1),mar=c(5,5,2,2))
g=50
x=advertising$TV-mean(advertising$TV)
y=advertising$Sales
b=sum((x-mean(x))*(y-mean(y)))/sum((x-mean(x))^2)
a=mean(y)-b*mean(x)
RSS.min=sum((y-as.vector(cbind(1,x)%*%c(a,b)))^2)/100000
a.grid=seq(a-2,a+2,length=g)
b.grid=seq(b-.02,b+.02,length=g)
grid=as.matrix(expand.grid(a.grid,b.grid))

RSS=rep(0,g^2)
for (i in 1:(g^2)){
yhat=as.vector(cbind(1,x)%*%grid[i,])
RSS[i]=sum((y-yhat)^2)/1000
}
RSS=matrix(RSS,g,g)
m=which.min(RSS)

contour(a.grid-b*mean(advertising$TV),b.grid,RSS,xlab=expression(beta[0]),ylab=expression(beta[1]),levels=c(2.11,2.15,2.2,2.3,2.5,3),axes=T,frame.plot=T,col=4,drawlabels=T,cex.lab=1.5,labcex=1.3)

points(a-b*mean(advertising$TV),b,col=2,pch=19,cex=1.5)

dev.off()

X_train = pd.read_csv('Data/Hitters_X_train.csv', index_col=0)
y_train = pd.read_csv('Data/Hitters_y_train.csv', index_col=0)
X_test = pd.read_csv('Data/Hitters_X_test.csv', index_col=0)
y_test = pd.read_csv('Data/Hitters_y_test.csv', index_col=0)

Hi, I clicked the link (http://www-bcf.usc.edu/~gareth/ISL/data.html) in the Chapter 3 Jupyter Notebook but it directed me to (http://faculty.marshall.usc.edu/gareth-james/) and I can't find the data there.

I fix it using:

fit3 = sm.GLM(df_steps.wage, df_steps_dummies.drop([df_steps_dummies.columns[1]], axis=1)).fit()

Hi, when I exercuted the following snippet within chapter-8 Tree-based Methods.

graph2 = print_tree(clf, features=X2.columns, class_names=['No', 'Yes'])
Image(graph2.create_png())

Ipython gave me some error message

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-15-fc4f6f6b365b> in <module>()
      1 graph2 = print_tree(clf, features=X2.columns, class_names=['No', 'Yes'])
----> 2 Image(graph2.create_png())

AttributeError: 'list' object has no attribute 'create_png'

My python enviroment is anaconda python 3.5 + pydot 1.2.3. Any suggestion to fix this problem? Thanks

http://stats.stackexchange.com/questions/160096/what-are-the-differences-between-ridge-regression-using-rs-glmnet-and-pythons