Full Stack Web App: Stocks Dashboard
Building and deploying a full-stack web application with a Python backend, JavaScript frontend, and containerized cloud deployment.
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A multi container ML app (1/3): Docker
Building a translation app by putting together 3 containerized microservices: a Flask frontend, a FastAPI backend and a MySQL database. Let’s skim through the development process and the containerization. Also covered: Docker registry and CI/CD with GitHub Actions.
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An asymmetric loss for regression models
Drive regression models towards under/overestimation while keeping accurate outputs with the linear-exponential loss.
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Interpretable machine learning with SHAP
In this post, we predict health insurance costs with an efficient black box model, namely random forest. Then we interpret individual predictions as well as the global behavior of the estimator using SHapley Additive exPlanations.
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Image recognition with PyTorch and fastai
Computer vision is one of the most fascinating domains in Machine Learning. Libraries like PyTorch and more recently, fastai, have made these kinds of models extraordinarily accessible. In this post, we build an aircraft classifier from gathering data to training and deployment.
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Gradient tree boosting in the cloud
A cloud computing experiment with two slightly different implementations of gradient boosted trees LightGBM and XGBoost. Let us evaluate how these two algorithms do on a moderately large dataset, regarding both accuracy and speed.
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Adding totals and subtotals rows with pandas or the tidyverse
When dealing with a dataframe, generating aggregate data is a very common task. In my experience, presenting the summary statistics for the whole population or for subgroups directly in the dataframe can be useful, if not necessary. Today, I present my recipe to achieve this with the pandas and tidyverse packages.
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Back to basics: Scaling train and test samples.
Splitting and scaling a dataset seems easy. Well, it is admittedly not that hard, however it can be tricky. Today we will see how to properly split and scale a dataset, as this step if often necessary before any ML wizardry. Let us do this with a few R & Python packages/modules.
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Weighted Random Forest with Spark 3
The third version of the number one distributed computing framework Spark was released in June 2020. Sample weights support was implemented for tree-based algorithms: decision tree, gradient tree boosting and random forest. Today we experiment with this new feature on an imbalanced dataset about credit card fraud.
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Outlier detection
In this post, I try to define what an outlier is and I present several ways to approach the problem of anomaly detection. Then, I present the Local Outlier Factor algorithm and apply it on a specific dataset to show its power, using both Python and R. I also compare its performance with the Isolation Forest method.
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