Automatic MLFlow logging for Scikit Learn

Marton Trencseni - Fri 15 January 2021 • Tagged with mlflow, tracking

I explore the automatic logging capabilities of MLFlow for Scikit Learn. In the process I found a bug in MLFlow, reported it and wrote a pull request to fix it.

MLFlow scatter plot.

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Getting Started with MLFlow

Marton Trencseni - Sun 10 January 2021 • Tagged with mlflow, tracking

For the last few months I’ve been using MFlow in production, specifically its Tracking component. MLFlow is an open source project for lifecycle tracking and serving of ML models, coming out of Databricks. MLFlow is model agnostic, so you can use with SKLearn, XGBoost, Pytorch, Tensorflow, FBProphet, anything.

MLFlow overview

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Making statistics lie for the 2020 Presidential election

Marton Trencseni - Thu 17 December 2020 • Tagged with ab-testing

After the 2020 US presidential election, the Trump campaign filed over 50 lawsuits and attacked the integrity of the elections by claiming there was voter fraud. One of the last lawsuits was filed in the Supreme Court of the United States by the state of Texas. Here I look at the statistical claims made in this lawsuit that were supposed to show irregularities in the Georgia vote.

Trump vs Biden

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Comparing conversion at control and treatment sites

Marton Trencseni - Thu 03 December 2020 • Tagged with ab-testing

In real-life, non-digital situations, it's often not feasible to run true A/B tests. In such cases, we can compare before and after rollout conversions at a treatment site, while using a similar control site to measure and correct for seasonality. The post discusses how to compute increasingly correct p-values and bayesian probabilities in such scenarios.

Monte Carlo simulated control lifts

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Unevenness at the edges

Marton Trencseni - Fri 30 October 2020 • Tagged with stats, data

Sometimes we look at the top performers in a field and see obviously uneven representations of groups (gender, ethnicity, etc). There a multitude of factors that can lead to it, such as unfair bias in access to opportunities. Here I will show one unintuitive mathematical effect that can contribute to such unevenness in the case of normal distributions.

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Effective Data Visualization Part 3: Line charts and stacked area charts

Marton Trencseni - Tue 01 September 2020 • Tagged with charts, dashboards, data, visualization

Most charts should be line charts or stacked area chart, because they communicate valuable trend information and are easy to parse for the human eyes and brain.

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Effective Data Visualization Part 2: Formatting numbers

Marton Trencseni - Sun 23 August 2020 • Tagged with charts, dashboards, data, visualization

Format numbers for human consumption. What is more readable, 1.539e+5 or 153,859? Showing numbers effectively on spreadsheets, charts, dashboards, reports is a basic ingredient for readability, like formatting code in programming.

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Effective Data Visualization Part 1: Categorical data

Marton Trencseni - Sat 22 August 2020 • Tagged with charts, dashboards, data, visualization

Making clear, readable charts is part of the craftmanship minimum for any data related role. In part one, I look at how to present categorical data.

A pie chart

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Multi-armed bandits and false positives

Marton Trencseni - Fri 21 August 2020 • Tagged with ab-testing

I use Monte Carlo simulations to explore the false positive rate of Multi-armed bandits.

Epsilon-greedy

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A/B testing and Multi-armed bandits

Marton Trencseni - Fri 07 August 2020 • Tagged with ab-testing

Multi-armed bandits minimize regret when performing A/B tests, trading off between exploration and exploitation. Monte Carlo simulations shows that less exploration yields less statistical significance.

Epsilon-greedy

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