emredjan/isl-python

Porting the R code in ISL to python. Labs and exercises

ISL-python

Porting the R code in Introduction to Statistical Learning to Python.

Inspired by and sometimes borrowed from Jordi Warmenhoven's and Hyun Bong Lee's excellent repos.

Mainly Labs and some exercises are ported. The jupyter notebooks are in labs and exercises folders respectively.

I'm trying to update the code, as I learn new tricks with scikit-learn and other libraries.

• Chapter 2 [Labs] [Exercises] - Introduction
• Chapter 3 [Labs] [Exercises] - Linear Regression
• Chapter 4 [Labs] [Exercises] - Classification
• Chapter 5 [Labs] [Exercises] - Resampling Methods
• Chapter 6 [Labs 1] [Labs 2] [Labs 3] [Exercises] - Linear Model Selection and Regularization
• Chapter 7 [Labs] [Exercises] - Moving Beyond Linearity
• Chapter 8 [Labs] [Exercises] - Tree-Based Methods
• Chapter 9 [Labs] [Exercises] - Support Vector Machines
• Chapter 10 [Labs 1] [Labs 2] [Labs 3] [Exercises] - Unsupervised Learning

isl-python's Issues

conf_m of LDA

Hi, I started doing this course - and similar to you, I want to implement it in Python.
I was going through your code and on the Notebook lab_04.6_logistic_regression_lda_qda_knn, if you scroll down to the 4.6.3 Linear Discriminant Analysis, I think there is a mistake in the code.

When getting the confusion matrix of the LDA you have the line conf_m = pd.DataFrame(confusion_matrix(y_test, glm_fit.predict(X_test))), but I believe you meant to have lda_fit.predict(X_test)? Since this section is for LDA, the glm_fit.predict() was used before in the Logistic Regression part.