AI News, Titanic – Machine Learning From Distaster with Vowpal Wabbit

Titanic – Machine Learning From Distaster with Vowpal Wabbit

Good clicklog datasets are hard to come by.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

tinrtgu posted a very cool benchmark on the forums that uses only standard Python libraries and under 200MB of memory.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

Predicting CTR with online machine learning

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

Identifying (and serving) those ads that have a higher probability of a click, translates into more profit and a higher quality: Behavorial retargeting is a form of online advertising where the advertisements are targeted according  to previous user behavior, often in the case where a visit did not result in a sale or conversion.

The engine is capable of predicting effective personalized advertisements on a web scale: real-time optimization of ad campaigns, click-through rates and conversion.

behavioral numerical feature may be the count of previous purchases. A behavioral categorical feature may be the product ID that was added to a shopping cart, but not purchased.

The engineering blog also shows how Criteo is creating web graphs (without using web crawlers) to engineer new features.

The web as seen by Criteo [from the Criteo Engineering blog] For this contest Criteo’s R&D division, CriteoLabs, has released a week’s worth of click data.

We have 13 columns of integer features (mostly count features), and 26 columns with hashed categorical features.

Though the exact nature of the features is unknown to us, according to a competition admin (Olivier Chapelle), they fall in the following categories: Our task now is to create a model that will predict the probability of a click.

think that sticking with your favorite machine learning tool or algorithm for all classification and regression problems, is like picking a chess opening and only playing that against all opponents.

It won’t perform the best on all competitions, though it will perform in most (Multiclass Classification, Regression, online LDA, Matrix Factorization, Structured Prediction, Neural network reduction, Feature interactions), and is a robust addition to many ensembles.

The collected click data is huge (often far larger than fits into memory) or unbounded (you may constantly collect new categorical feature values).

That the competition metric really is logaritmic loss means we gain a massive amount of information even by training a model with Vowpal Wabbit: With its holdout functionality one-in-ten samples will be used to calculate and report on the average loss.

(Edit: Thanks to Anuj for spotting that I forgot to specify the model when testing, code above updated.) After running the above command we should have our predictions in a text file (~100MB).

The process for creating baseline benchmark was: We beat the logistic regression benchmark with our first submission. Vowpal Wabbit truly is an industry-ready tool for machine learning on large and high dimensional datasets.

SQuAD

Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage.

Download a copy of the dataset (distributed under the CC BY-SA 4.0 license):Training Set v1.1 (30 MB)Dev Set v1.1 (5 MB) To evaluate your models, we have also made available the evaluation script we will use for official evaluation, along with a sample prediction file that the script will take as input.

Titanic – Machine Learning From Distaster with Vowpal Wabbit

Good clicklog datasets are hard to come by.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

tinrtgu posted a very cool benchmark on the forums that uses only standard Python libraries and under 200MB of memory.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

How to get into the top 15 of a Kaggle competition using Python

Kaggle competitions are a fantastic way to learn data science and build your portfolio.

In this post, I'll cover how to get started with the Kaggle Expedia hotel recommendations competition, including establishing the right mindset, setting up testing infrastructure, exploring the data, creating features, and making predictions.

The Expedia competition challenges you with predicting what hotel a user will book based on some attributes about the search the user is conducting on Expedia.

Looking over this, it appears that we have quite a bit of data about the searches users are conducting on Expedia, along with data on what hotel cluster they eventually booked in test.csv and train.csv.

Since the competition consists of event data from users booking hotels on Expedia, we'll need to spend some time understanding the Expedia site.

The box labelled Check-in maps to the srch_ci field in the data, and the box labelled Check out maps to the srch_co field in the data.

user_location_country, user_location_region, user_location_city, is_mobile, channel is_booking, and cnt are all attributes that are determined by where the user it, what their device is, or their session on the Expedia site.

Once we download the data, we can read it in using Pandas: Let's first look at how much data there is: We have about 37 million training set rows, and 2 million testing set rows, which will make this problem a bit challenging to work with.

We can explore the first few rows of the data: There are a few things we can take away from looking at test.csv: We'll be predicting which hotel_cluster a user will book after a given search.

The evaluation page says that we'll be scored using Mean Average Precision @ 5, which means that we'll need to make 5 cluster predictions for each row, and will be scored on whether or not the correct prediction appears in our list.

cluster is 3, and we predict 4, 43, 60, 3, 20, our score will be lower than if we predict 3, 4, 43, 60, 20.

We can use the value_counts method on Series to do this: The output above is truncated, but it shows that the number of hotels in each cluster is fairly evenly distributed.

By selecting both sets from train.csv, we'll have the true hotel_cluster label for every row, and we'll be able to calculate our accuracy as we test techniques.

Because the train and test data is differentiated by date, we'll need to add date fields to allow us to segment our data into two sets the same way.

The code below will: Because the user ids in test are a subset of the user ids in train, we'll need to do our random sampling in a way that preserves the full data of each user.

We can accomplish this by selecting a certain number of users randomly, then only picking rows from train where user_id is in our random sample of user ids.

In the original train and test DataFrames, test contained data from 2015, and train contained data from 2013 and 2014.

We can again use the value_counts method to help us here: The above code will give us a list of the 5 most common clusters in train.

This is because the head method returns the first 5 rows by default, and the index property will return the index of the DataFrame, which is the hotel cluster after running the value_counts method.

We can compute our error metric with the mapk method in ml_metrics: Our target needs to be in list of lists format for mapk to work, so we convert the hotel_cluster column of t2 into a list of lists.

We can find linear correlations in the training set using the corr method: This tells us that no columns correlate linearly with hotel_cluster.

Unfortunately, this means that techniques like linear regression and logistic regression won't work well on our data, because they rely on linear correlations between predictors and targets.

This data for this competition is quite difficult to make predictions on using machine learning for a few reasons: For these reasons, machine learning probably won't work well on our data, but we can try an algorithm and find out.

Here's a sample: The competition doesn't tell us exactly what each latent feature is, but it's safe to assume that it's some combination of destination characteristics, like name, description, and more.

We can use the destination information as features in a machine learning algorithm, but we'll need to compress the number of columns down first, to minimize runtime.

In the code below, we: The above code compresses the 149 columns in destinations down to 3 columns, and creates a new DataFrame called dest_small.

Cross validation splits the training set up into 3 parts, then predicts hotel_cluster for each part using the other parts to train with.

We'll first initialize the model and compute cross validation scores: The above code doesn't give us very good accuracy, and confirms our original suspicion that machine learning isn't a great approach to this problem.

Differences in leaderboard score and local score can come down to a few factors: The forums are very important in Kaggle, and can often help you find nuggets of information that will let you boost your score.

This post details a data leak that allows you to match users in the training set from the testing set using a set of columns including user_location_country, and user_location_region.

In order to do this, we need to: Here's the code to accomplish this: At the end of this loop, we'll have a list of lists that contain any exact matches between the training and the testing sets.

If you want to learn more before diving into the competition, check out our courses on Dataquest to learn about data manipulation, statistics, machine learning, how to work with Spark, and more.

IBM Sets Tera-scale Machine Learning Benchmark Record with POWER9 and NVIDIA GPUs; Available Soon in PowerAI

The AI software behind the speed-up is a new library developed over the past two years by our team at IBM Research in Zurich called IBM Snap Machine Learning (Snap ML) – because it trains models faster than you can snap your fingers.

The library provides high-speed training of popular machine learning models on modern CPU/GPU computing systems and can be used to train models to find new and interesting patterns, or to retrain existing models at wire-speed (as fast as the network can support) as new data becomes available.

This long turn-around time (from data preparation to scoring) can be a severe hindrance to the research, development and deployment of large-scale machine learning models for critical applications such as weather forecasting and financial fraud detection.

In order to design a winning ensemble, a data scientist typically spends a significant amount of time trying out different combinations of models and tuning the large number of hyper-parameters that arise.

One such application is click-through rate prediction in online advertising, where it has been estimated that even 0.1% better accuracy can lead to increased earning of the order of hundreds of millions of dollars.

Whether a small to medium business is running in the cloud or a large-scale enterprise IT operation, which services many business units, machine learning puts pressure on compute resources.

We focus on the training of generalized linear models for which we combine recent advances in algorithm and system design to optimally leverage all hardware resources available in modern computing environments.

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