Python-bloggers

How to Implement a Gradient Boosting Machine that Works with Any Loss Function

October 22, 2021 | Matt Bowers

Cold water cascades over the rocks in Erwin, Tennessee. Friends, this is going to be an epic post! Today, we bring together all the ideas we’ve built up over the past few posts to nail down our understanding of the key ideas in Jerome Friedma...

[...Read more...]

Personal Highlights of Scikit-Learn 1.0

October 21, 2021 | Christian Lorentzen

Yes! After more than 10 years, scikit-learn released its 1.0 version on 24 September 2021. In this post, I'd like to point out some personal highlights apart from the release highlights. 1. Feature Names This one is listed in the release highlights, but deserves to be mentioned again. This is not yet available for all […] [...Read more...]

RvsPython #6: LinkedIn has spoken!

October 18, 2021 | Benjamin Smith

Introduction Over the past while with my time on LinkedIn, I got to have exposure to many people from many different lines of work. I also managed to have carved a space for myself there where I can post about Data Science topics and share my blogs along the way. ...

[...Read more...]

CNN For Image Classification: Does The Neural Network Really See The Seeds?

October 17, 2021 | Gaja Klaudel

Open a bag of garden variety seeds and take a close look. What do you see? Is it a healthy seed? A broken seed? Could you accurately predict if that seed will grow? That’s what the Świeżewski lab sought to answer using Computer Vision models. And as it ...

[...Read more...]

CNN for Image Classification: Does the Neural Network Really See The Seeds?

October 17, 2021 | Gaja Klaudel

Could you look at a seed and predict if it will grow? That’s just what the Świeżewski lab sought to answer and ultimately has achieved by identifying morphological parameters of thale cress (Arabidopsis thaliana) seeds and applying this discovery to Computer Vision models. By using a CNN for ...

[...Read more...]

Data Algebra 0.9.0 Release

October 9, 2021 | John Mount

I am pleased to announce the 0.9.0 release of the data algebra. The data algebra is realization of the Codd relational algebra for data in written in terms of Python method chaining. It allows the concise clear specification of useful data transforms. Some examples can be found here. Benefits include […] [...Read more...]

Visualizing trees with Sklearn

October 6, 2021 | Nagdev Amruthnath

Tree-based models are probably the second easiest ML technique for explaining the model to a non-data scientist. I am a big fan of tree-based models because of their simplicity and interpretability. But, when I try to visualize them is, when it gets my nerves. There are so many packages out ...

[...Read more...]

Multi-Output Regression using Sklearn

October 6, 2021 | Nagdev Amruthnath

Regression analysis is a process of building a linear or non-linear fit for one or more continuous target variables. That’s right! there can be more than one target variable. Multi-output machine learning problems are more common in classification than regression. In classification, the categorical target variables are encoded to ... [...Read more...]

Python Musings #7: Simulating FSAs in lieu of real postal code data.

October 1, 2021 | Benjamin Smith

Disclaimer The following is the same content that I have posted in my other blog on this topic in R, but written in Python. While I actually first wrote the code for doing this in Python, I’ll be posting the similar verbiage from that blog here. Introduction Often when ... [...Read more...]

Using the data algebra for Statistics and Data Science

September 28, 2021 | John Mount

I have a new intermediate introduction on the data algebra up here: Using the data algebra for Statistics and Data Science. The data algebra is a tool for data processing in Python which is implemented on top of any of Pandas, Google BigQuery, PostgreSQL, MySQL, Spark, and SQLite. It allows […] [...Read more...]

« 1 … 41 42 43 44 45 … 98 »