We then generate the same features as above. If the shopper has bought from the brand, category and company before we generate a specific feature for that. This is a constant for every offer. The offer value might influence the number of repeat buyers. The offer value fluctuates between about 5 and 0.
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We also get the offer quantity how many items can be redeemed with the coupon. We think this may influence number of repeat buyers. We are interested to see how much the shopper spend. For every transaction still in the reduced data set we take the amount and add it all up. We think that total shopper spend will influence future chance of repeat buys.
We have now generated a test set and a train set with our features.
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A line from the train set could look like:. Now we run Vowpal Wabbit we use version 7. We can use this to check how relevant our features are. If we run the output of vw-varinfo through our plotfeatures. We are almost there. We have a file with predictions our output from Vowpal Wabbit and we need to turn this into the Kaggle submission format. We lack predictions for about shoppers as their transaction data did not include any product from a category, brand or company on offer. We predict a 0 for these cases.
Our first submission scored position 3 on the public leaderboard.
After some tweaking it increased the score and is again at position 3. Using Python, a just-has-to-work mentality and the magnificent tool Vowpal Wabbit we were able to create a competing submission in a few hours. The scripts takes about 15 minutes to produce this submission from the raw data. It takes under 1 GB of memory and will thus run even on budget laptops.
You can find all code in the MLWave Github repo. Happy competition! When i run. Hi, Triskelion, thanks for your reply. I got another problem…. My computer is win7 32bit. Maybe i should try this under Unbuntu…. Thanks for your time. Heya douhuang! Another reader had found a problem with that tutorial too. Hang on! I had understood quantile regression to be performed on continuous variables and not logistic models. Is there something that is happening during the quantile regression?
I think, since we are predicting probabilities of repeat trips, that logistic loss should be optimized if I understand it correctly. Minimizer: expectation Logistic loss: Probability of click on advertisement minimizer: probability. Do note that VW does a little more than just regression. Its default is a normalized adaptive importance-invariant update rule.
Also quantile regression is good for ranking, which I think helps with AUC evaluation. I will soon, once I find the time to optimize my current score basically this benchmark with a few tweaks. I have a pretty stupid question but from this whole tutorial there was one thing that I could not figure out.
I spent many hours trying to find them while I know it must be something really simple…. Hi Triskelion, Thanks a lot for this informative blog. I have used logistic regression to build my model and I am getting a score of I am now trying quantile regression using vowpal wabbit. I had a couple of queries regarding quantile regression: a. Do the variables that I use need to be relatively free of correlation as is the case with logistic regression?
Or is it fine if the variables have large correlation between them and vowpal wabbit takes care of it? Do the variables need to have monoticcally increasing or decreasing trend w.
Lets take an exmaple of variable like number of transactions. Do the response rates have to increase with increasing number of transactions for quantile regression to work well? Does quantile regression give out probabilities which match this trend? I ve tried to run your features in VW. Your python script worked really well for me. But I ve problems running it in VW.
I start VW with.
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What am I missing? Where I have to save my VW files? Can you do better? The Confidence Challenge requires competitors to assign a level of confidence to their World Cup predictions. This competition requires contestants to forecast the voting for this years Eurovision Song Contest in Norway on May 25th, 27th and 29th. The Most Comprehensive List of Kaggle Solutions and Ideas This is a list of almost all available solutions and ideas shared by top performers in the past Kaggle competitions.
Open Images - Object Detection Detect objects in varied and complex images. Open Images - Visual Relationship Detect pairs of objects in particular relationships.
Open Images - Instance Segmentation Outline segmentation masks of objects in images. Recursion Cellular Image Classification CellSignal; Disentangling biological signal from experimental noise in cellular images.
Ciphertext Challenge III -. Predicting Molecular Properties Can you measure the magnetic interactions between a pair of atoms? Generative Dog Images Experiment with creating puppy pics. Jigsaw Unintended Bias in Toxicity Classification Detect toxicity across a diverse range of conversations. Aerial Cactus Identification Determine whether an image contains a columnar cactus. Instant Gratification A synchronous Kernels-only competition.
Freesound Audio Tagging Automatically recognize sounds and apply tags of varying natures.
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Google Landmark Recognition Label famous and not-so-famous landmarks in images. Google Landmark Retrieval Given an image, can you find all of the same landmarks in a dataset? Dont Overfit! II A Fistful of Samples. Ciphertext Challenge II -.
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