{"action":"created","comment":{"url":"https://api.github.com/repos/sigopt/sigopt-examples/pulls/comments/54481552","id":54481552,"diff_hunk":"@@ -0,0 +1,438 @@\n+{\n+ \"cells\": [\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"# SigOpt for ML: Automatically Tuning Text Classifiers Walkthrough\\n\",\n+    \"*This notebook is a walkthrough companion for our blog post [SigOpt for ML: Automatically Tuning Text Classifiers](http://blog.sigopt.com/post/133089144983/sigopt-for-ml-automatically-tuning-text) by Research Engineer [Ian Dewancker](https://sigopt.com/about#ian)*\\n\",\n+    \"\\n\",\n+    \"Text classification problems appear quite often in modern information systems, and you might imagine building a small document/tweet/blogpost classifier for any number of purposes. In this example, we're building a pipeline to label [Amazon product reviews](http://www.cs.jhu.edu/~mdredze/datasets/sentiment/) as either favorable or not. First, we'll use scikit-learn to build a logistic regression classifier, performing k-fold cross validation to measure the accuracy. Next, we'll use SigOpt to tune the hyperparameters of both the feature extraction and the model building. \\n\",\n+    \"## Setup\\n\",\n+    \"If you’d like to try running this example, sign up for a [SigOpt](https://sigopt.com/) free trial and find your newly created credentials on your [profile page](sigopt.com/user/plan).  To quickly set up an environment to run this example, we recommend using a Mac OS X (with brew) or an Ubuntu machine having at least 4GB of RAM (t2.mediums in EC2 are cheap and work great).  Once you have a suitable machine this script should be all you need to get to get started:\\n\",\n+    \"```\\n\",\n+    \"# on Mac OS X or Ubuntu machine\\n\",\n+    \"git clone https://github.com/sigopt/sigopt-examples.git\\n\",\n+    \"cd sigopt-examples/text-classifier/\\n\",\n+    \"sudo ./setup_env.sh\\n\",\n+    \"```\\n\",","path":"text-classifier/SigOpt Text Classifier 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