The document outlines a workflow for predicting click-through rates using logistic regression with ridge regularization on the Criteo Kaggle dataset. It details steps such as data loading, one hot encoding of categorical features, training a classifier, optimizing regularization parameters, and generating ROC curves for evaluation. Comparisons are made between a baseline model and optimized logistic regression performance.

1. CTR Logistic Regression
Criteo Kaggle dataset
One Hot Encoding
ROC curves
Joe Duimstra
Sept 1, 2015

2. Workflow
In an iPython notebook, I predict Click Through Rates using logistic
regression with ridge regularization
1. Get the data
2. Load and split data into training, cross-validation, and test sets
3. Use One Hot Encoding (OHE) to generate a sparse matrix from
the categorical features in the dataset
4. Train a logistic regression classifier using log loss as the
evaluation metric and compare to a baseline model that predicts
the same value regardless of observation
5. Optimize classifier by finding best regularization parameter
6. Generate ROC curves
7. Use the model to predict the test set outcomes

3. Get the Data

Data is from Criteo's Kaggle competition

4. Generate OHE dictionary

Encode each unique feature value as a unique integ

5. Train LR classifier

Use the training data to train a logistic regression
classifier

Generate a baseline model for comparison

6. Optimize regularization parameter

7. ROC curves
●
Blue is train set
●
Green is unoptimized logistic regression
●
Red is optimized logistic regression

8. Comparing with PySpark