Another treat! Early access to Intro To Machine Learning videos

(Sumit) #709


Please run this conda install -c anaconda graphviz .
Let me know if it doesn’t work.

(Sumit) #710


I’m facing the same issue but couldn’t able to solve it.
After looking into forums i ran this conda install -c defaults intel-openmp -f but nothing happend & also i don’t know what’s the significance of it.

Have you solved it? Can you please help me.


(sashank) #711

yes , its the same … I later realized that feature logic is not working in my system now sure why .

(Sumit) #712

Can you tell what’s the shape of df and y?
& what’s showing after you run proc_df , I mean any error or anything which will be helpful to figure what’s wrong ?

and also if at all df & y has values then can you post first 4-5 rows in here .


(Erick Giffoni) #713

Thank you. It worked.

(Aditya) #714

Check your mem

(Aditya) #715

This is amazing!!

(Sumit) #716

can anyone please help me.



Hi Jeremy since these videos are not to be found on and only on youtube am I right in presuming that they have not been formally launched as of yet for the ML course

(Yoong Kang Lim) #718

Hey everyone! First post here.

In the video for lesson 7, about 18 minutes in @jeremy makes a point about imbalanced datasets. He mentioned a recent paper that looked at some approaches to deal with this and concluded that oversampling from the smaller category wins out consistently.

Has anyone tracked down this paper? I’d love to have a look at it.

(sashank) #719

When should we use One Hot Encoding & Categorical Codes . Can anyone help me on this ?
For eg : for countries what should we use ?

(Kieran) #720

Use the train_cats() function which takes your data frame and changes the categorical variables to data type (dtype) category. Then when we call proc_df() these categorical variables will be changed to numeric variables. Its important that they all have the same spelling etc!

Check out 7:58 on Machine Learning 1 lesson 2 video.

Hope that helps

(Kieran) #721

My guess is that its one of these on this scholar search.
Problem is they tend to be uber technical and in general you have to pay for them.

Let me know if you find anything interesting :slight_smile:


Hey, I’m also facing the very same issue. My kernel gets restarted when trying to run df, y, nas = proc_df(df_raw, ‘SalePrice’). This issue is seen with a dataframe size of 100. Where you able to resolve this issue?

(Sanyam Bhutani) #723

Cross Posting here for visibility:

I’ll host weekly discussions starting on the 12th of August. (ML MOOC)

Please take a second to vote for timings if you’re interested .



Additionally to those, I had to install:

  • pip install isoweek
  • pip install pandas-summary

To find out which dependencies you might be lacking, it is useful to start an interactive python session just by typing “python” on the command line prompt and then running
from fastai.imports import *
at the python “>>>” prompt until you get no errors. Any errors there should show the missing dependencies.

(Axel Straminsky) #725

In one of the lectures Jeremy showed a library for interpreting random forests, but if I remember correctly he said that he didn’t know of a library that did the same for Neural Nets. A few days ago I came across a new library called SHAP, that apparently is not only for interpreting RF, but any ML model. Has anyone tried it?


(sashank) #726

can anyone help me on this ?

(Kevin Bird) #727

I am listening to lesson 5 and I am not sure I understand the extrapolation section. So you try to predict your validation set records (in my case, I have a holdout set) Then you take the feature importance of those and try to drop each of them and run the model like that. At that point I would expect you to keep the columns that would make the score worse if it weren’t in the model and drop anything that makes it better, but Age which when dropped doesn’t hurt but you still keep it in. Why is this not also dropped? I have tried implementing this in a real world scenario and I am not getting any of my columns that are making the model better when they shouldn’t, but when I predict a previous month and remove all the data after that point, I get fairly decent results, but when I try to predict the following month, I am not getting as good of results. I suspect data leakage of some sort, but I haven’t tracked it down yet.

(Kevin Bird) #728

I have used it and from what I can tell it does a pretty good job. I don’t think it is directly interpreting the model though so it is taking a simplified version of the model to make it’s assumptions I think. So sometimes the Feature importance list from the model will be different from the SHAP library. Overall I definitely think it has a lot of potential. I’ve been using it with XGBoost with pretty decent success.