Some sample IPython notebooks for scikit-learn
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letor_cluster
representations
screenshots
sklearn_demos
.gitignore
Adjusted Variable Importances with Randomized Trees.ipynb
Bootstrap.ipynb
Clustering Model Selection.ipynb
Data Preprocessing for the Learning to Rank example.ipynb
Distributed Aggregate and Join.ipynb
Distributed Learning of Extra Trees with IPython.parallel.ipynb
Explained variances.ipynb
Labeled Faces in the Wild recognition.ipynb
Learning to Rank.ipynb
MNIST8M Chunking and Upload to Cloud Blob Storage.ipynb
Non IID cross-validation.ipynb
Numa-aware computation experiments.ipynb
Numba Parakeet Cython.ipynb
Numpy intro.ipynb
Parameter search for Extra Trees on the MNIST classificationt task.ipynb
Patch-Based Feature Extraction for Image Classification.ipynb
README.md
SGD stuff.ipynb
Semi-supervised Extra Trees.ipynb
Text Classification.ipynb
Time Series.ipynb
Variable Importance with Completely Randomized Trees.ipynb
cloudstorage.ini.example
nmf_topics.ipynb
structure_digits.ipynb
ubuntu-quickstart.sh

README.md

Sample IPython 0.12+ notebooks for machine learning stuff

Screenshots

Digits Topics

Install dependencies

sudo pip install -U tornado
sudo pip install -U pyzmq
sudo pip install -U git+https://github.com/ipython/ipython.git

Run the notebook

Then cd into this folder and run:

ipython notebook --pylab=inline

Then click on a notebook. The focus is on the first cell: hit Shift-Enter to execute the current cell an move on to the next. You can also click on Run > All in the left panel.