The first prediction resulted in 10k rows of 0s. I was surprised. Why it went like this? Did none of them have some feat
Their framework is easy to work with, specifying layers similar to MLPClassifier in sklearn — pass a list with neuron count in each layer. Though running your training on CPU will drive you mad (it takes A LOT of time, like literally A LOT). In my case, when I used the CPU, it showed 150 hours. None of my owned machines had an Nvidia GPU inside, so it wasn’t on my list to try first. In later parts, I’ll tell how to use free GPU on some platforms and speed up any model training that can leverage GPU.
The most common (or at least it was for me) understanding that sklearn is the most used library for ML. Before going to baseline results, I’m going to present some of the libraries. Keep in mind I’m aiming here at fresh Data Scientist or this path explorers, so I’m not going to Neural Network libraries (Keras, Tensorflow, PyTorch).
I was not too fond of that H2OFrame doesn’t quite work like pandas/spark/dask data frames sometimes. It was a bit hard to figure out how to do certain things, i.e., get a list of distinct column values. Here I had to use flatten, though in pandas would be list(). Nonetheless pretty cool library which can help anyone!
Due to Pick’n’Mix’s un-opinionated stance on how things should be built, each app had to maintain its build pipelines and menus/games had to orchestrate their own interaction with the GMI, ultimately there was no sharing or composability between apps. On the surface, abstracting native functionality and business logic behind the GMI seemed sensible and good value for money, a single, well-defined API for fetching remote data, right?! Digging into this further though, it was clear that we’d simply offset the cost away from agencies having to build the native functionality (which is good in itself) to agencies having to integrate with an API that was just as complex and potentially more un-intuitive than if we did nothing at all.
How many times you ran a model, got a good score, but the next day you couldn’t reproduce because you’ve made some changes here and there (different configuration, dataset)? At least to me, it was getting out of hand quite fast. Only printing some configuration, and the AUC score was getting lost pretty fast. I needed to track all of the configurations, scores, and data in general somehow. Using this platform, I could have logged my best model and then fine-tune it. Best discovery for me, though it was a bit too late for the challenge.
and access your data in the usual way, just from /content/drive. Though I guess you agree with google seeing your data, which might not work for sensitive information. Though for Kaggle and playing around, it works quite well.
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hold with no money and no qualifications, Lee grew up into a restless short-tempered youth, wanted to earn money fast and see the world. He enlisted in the Royal Navy but had to pull out within a year due to a serious leg injury that left him invalidated.This snippet would fix imbalances, remove outliers and fit multiple models with a search on hyperparameters. My data was around 1GB CSV file, so even 5 folds take some time on one model. Keeping in mind that there is hyperparameter tunning multiple models 5 folds = too long waiting time. If I’d have smaller datasets, I’d definitely give it a try again; it seems easy to work with, and it does all the work for you!
And apparently there was! Library imblearn is dedicated to help you solve this issue! You can choose different methods of oversampling, under-sampling, or even a combination of them.
This also led to us having significant amounts of duplication across our portfolio of apps. UX patterns implemented time and time again written imperatively with WebGL/Canvas, several integrations and duplicated business logic around how downloads should be handled under certain network conditions, inconsistent bundling tools and inconsistent config approaches across apps.
This one is a library for doing ML, similar to sklearn. Though it has UI, on which you can do your ML by clicking multiple mouse buttons. Import data, explore it, impute missing data if you’d like, and then even run AutoML, which will search on models and configuration to find the best one (also stacks them at the end!). I’d say it can help even a person having almost none programming experience to do ML.
Let’s do a hyperparameter tuning for them. Usual helpers are GridSearchCV (exhaustive search) or RandomSearchCV (not necessarily get the best combination) from sklearn. I was so glad that I found out about this amazing gem — scikit-optimize. It has BayesSearchCV. It takes ranges and takes a parameter of several iterations. It should try to optimize your model based on the scorer you provide. Minor issue with this that it could fall to a local minimum, and you wouldn’t improve anywhere.
So let’s go with the latter one. At first, I manually extracted the minority class and repeated it until we have a more or less similar count. I noticed that it’s not efficient. If I try to do it per one dimension value, I quickly get lost. As usual with python, I thought there has to be a library for this.
Now your data preparation will be robust, and you will be sure that your train dataset and the one you’ll be basing your predictions will follow the same manipulations in the same order!
MLflow — A platform for the machine learning lifecycle An open-source platform for the machine learning lifecycle MLflow is an open-source platform to manage the ML…mlflow.org.