A Fine Slice Of SQL Server
Niraj Kumar explains how two regression techniques work:
Lasso Regression is a regularization technique used for feature selection using a Shrinkage method also referred to as the penalized regression method.
Lasso is short for Least Absolute Shrinkage and Selection Operator, which uses both for regularization and model selection.
If a model uses the L1 regularization technique, then known as lasso regression.
Click through for a summary of the two techniques.
Comments closedMark White does some coloration switcharoos:
I also love film, and I started thinking about ways I could generate color palettes from films that use color beautifully. There are a number of packages that can generate color palettes from images in R, but I wanted to try writing the code myself.
I also wanted to not just generate a color palette from an image, but then swapping it with a different color palette from a different film. This is similar to neural style transfer with TensorFlow, but much simpler. I’m one of those people that likes to joke how OLS is undefeated; I generally praise the use of simpler models over more complex ones. So instead of a neural network, I use k-means clustering to transfer a color palette of one still frame from a film onto another frame from a different movie.
There are some interesting outcomes in the post, including a mashup of 2001: A Space Odyssey’s color scheme onto Arrival, as well as Kill Bill and Dr. Strangelove. The latter reminds me of a still from the credits sequence to a 1970s movie. H/T R-Bloggers.
Comments closedMilecia McGregor looks at a version control system for ML projects (and data):
What happens when the machine learning model you’ve worked so hard to get to production becomes stale? Machine learning engineers and data scientists face this problem all the time. You usually have to figure out where the data drift started so you can determine what input data has changed. Then you need to retrain the model with this new dataset.
Retraining could involve a number of experiments across multiple datasets, and it would be helpful to be able to keep track of all of them. In this tutorial, we’ll walk through how using DVC, an open source version control system for machine learning projects, can help you keep track of those experiments and how this will speed up the time it takes to get new models out to production, preventing stale ones from lingering too long.
My team is working on integrating DVC. It’s a really good project for analytics teams, as it extends the notion of version control to datasets and helps you tie in code (source control), models (tools like MLflow), and data.
Comments closedVini Jaiswal reviews the Tensorflow Probability library:
In this blog, we look at the topic of uncertainty quantification for machine learning and deep learning. By no means is this a new subject, but the introduction of tools such as Tensorflow Probability and Pyro have made it easy to perform probabilistic modeling to streamline uncertainty calculations. Consider the scenario in which we predict the value of an asset like a house, based on a number of features, to drive purchasing decisions. Wouldn’t it be beneficial to know how certain we are of these predicted prices? Tensorflow Probability allows you to use the familiar Tensorflow syntax and methodology but adds the ability to work with distributions. In this introductory post, we leave the priors and the Bayesian treatment behind and opt for a simpler probabilistic treatment to illustrate the basic principles. We use the likelihood principle to illustrate how an uncertainty measure can be obtained along with predicted values by applying them to a deep learning regression problem.
Read on for an interesting explanation and tutorial.
Comments closedMark Zhang shows off a new bit of functionality in MLflow:
According to an internal customer survey, 75% of respondents say they frequently or always use specialized, business-focused metrics in addition to basic ones like accuracy and loss. Data scientists often utilize these custom metrics as they are more descriptive of business objectives (e.g. conversion rate), and contain additional heuristics not captured by the model prediction itself.
In this blog, we introduce an easy and convenient way of evaluating MLflow models on user-defined custom metrics. With this functionality, a data scientist can easily incorporate this logic at the model evaluation stage and quickly determine the best-performing model without further downstream analysis
Click through to see how to use built-in metrics but also how to create your own.
Comments closedBrendan Tierney uses Python to look at some data:
One of the most common, and sometimes boring, task when working with datasets is writing some code to profile the data. Most data scientists will have built a set of tools/scripts to help them with this regular and slightly boring task. As with most IT tasks we should be trying to automate what we can, to allow us to spend more time on more important tasks, such as deriving insights and delivering value to the business, instead of repeatedly writing code to produce various statistics about the data and drawing pretty pictures.
I’ve written previously about automating and using some data profiling libraries to help us with this task. There are lots of packages available on pypi.og and on GitHub. Below I give examples of 5 Python Data Profiling libraries, with links to their GitHubs.
Brendan includes some good examples of libraries here so check it out.
Comments closedJoey Jablonski brings up an important point:
Data-driven environments have a fundamentally different set of needs around testing, deployment, and visibility then traditional business applications. Data driven environments need access to fresh data on a high level of update frequency to ensure that data engineers and data scientists are able to effect outputs and recommendations on a timeline that has a positive impact on business decisions and customer experiences.
My day job involves running a predictive analytics team. We train models on production data—there’s very little value in training models on artificial dev data (outside of understanding the parameters of the modeling process), so even our development data generally comes from production. I don’t know that I’m sold on data mesh as a solution to this but it’s worth investigation.
Comments closedAlex Woodie reports on a purchase:
Cloud data warehousing giant Snowflake showed it’s serious about Python and data science this week when it announced that it plans to spend $800 million to buy Streamlit, a provider of Python-based tools for rapidly developing interactive data applications on the Web.
Co-founded in San Francisco in 2018 by Adrien Treuille, Amanda Kelly, and Thiago Teixeira, Streamlit develops an open source framework of the same name that allows data scientists and machine learning engineers to create and deploy data applications. The software is compatible with other Python-based frameworks, such as NumPy, Pandas, Matplotlib, and Scikit-learn, and uses React to render screens on the front-end.
Streamlit is nice. $800 million nice? That’s a good question.
Comments closedThe Finnstats folks talk about regression:
Basically, Regression analysis involves creating an equation to describe the significant association between one or more predictors and response variables, as well as estimating current observations.
The results of the regression reveal the direction, size, and analytical significance of the relationship between predictor and response, where the dependent variable is either numerical or discrete.
Click through for details on six types of regression. H/T R-Bloggers.
Comments closedJohn Cook has some thoughts on an interesting problem:
Suppose you want to determine how to price a product and you initially don’t know what the market is willing to pay. This post outlines some of the things you might think about, and how Bayesian modeling might help.
This post is not the final word on the subject, or even my final word on the subject. It is essentially a reply to a friend’s question turned into a blog post rather than an email.
This is a really interesting problem. Price is ultimately a signal rather than the solution. This is an attempt to understand how you start to build up enough information to start setting effective prices. Over a long enough timeframe, we’ll eventually land on a commonly accepted price but that’s an economist’s way of hand-waving away the process.
Comments closed