(testing signal)

Tag: fitting

How to Determine the Best Fitting Data Distribution Using Python – KDnuggets

Sometimes you know the best fitting distribution, or probability density function, of your data prior to analysis; more often, you do not. Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.

This is where distfit comes in.

distfit is a python package for probability density fitting across 89 univariate distributions to non-censored data by residual sum of squares (RSS), and hypothesis testing. Probability density fitting is the fitting of a probability distribution to a series of data concerning the repeated measurement of a variable phenomenon.… Read more...

Predicting Wine Prices with Tuned Gradient Boosted Trees

Using Optuna to find the optimal hyperparameter combination

Many popular machine learning libraries use the concept of hyperparameters. These can be though of as configuration settings or controls for your machine learning model. While many parameters are learned or solved for during the fitting of your model (think regression coefficients), some inputs require a data scientist to specify values up front. These are the hyperparameters which are then used to build and train the model.

One example in gradient boosted decision trees is the depth of a decision tree. Higher values yield potentially more complex trees that can pick up on certain relationships, while smaller trees may be able to generalize better and avoid overfitting to our outcome — potentially leading to issues when predicting unseen data.


5 Spectacular Features From Julia I Wish Were In Python

One thing that has to be loved about Julia by all programmers is its robust type system. For my personal preference when it comes to the strength of typing, Julia fits the bill quite well. That being said, Python sits around the same area in terms of the strength of the types, although perhaps a little more implicit with type changing. That being said, I think that Julia’s type-hierarchies and base data-types are far superior to that of Python’s.

This is not to say that Python’s type system is not robust, but there are certainly improvements that could be made. This is especially the case when it comes to inheritance of numerical types and iterables.


Got Skills, Need Cash? Why Not Try Consulting

If you are reading this, you’re probably a data scientist of one flavor or another. Or you have some technical chops, you like data, and you want to learn more about how data science skills can make you more marketable.

Whatever your motivation, if you fit either description above, consulting needs you. I realized early on that knowing how to work with data is a very valuable skill and lots of people and businesses are willing to pay others to help them with their data.

Despite the need, breaking into the field can still be a challenge. It isn’t enough to put up a website, let the world know what you can do, and wait.


7 Essential Features of Data Quality Tool

The availability of big data in the Digital Era enables new generation industries to create novel business models and automate their operations. It also assists them in developing innovative technology solutions that lead to new commercial opportunities. Sensors, machinery, social media, Web sites, and e-commerce portals all create large amounts of data. Any organization’s success is determined by the quality of the data it collects, stores, and uses to derive insights, and quality data is the foundation of any business and is found at the bottom of the information hierarchy. Data quality can be defined as a trait that makes data fit for its intended use, as well as a characteristic that allows data to accurately represent the genuine picture it is designed to portray.


KDnuggets™ News 21:n31, Aug 18: The Difference Between Data Scientists and ML Engineers; MLOPs And Machine Learning RoadMap

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What is the difference between Data Scientists and ML Engineers? How does MLOps fit into Machine Learning Roadmap? How to Train a BERT Model From Scratch? What is so great about Intro to Statistical Learning, 2nd Edition? Find the answers to these questions and more in this issue.

KDnuggets Top Blogs Reward Program will pay to the authors of top blogs each month. Reposts accepted, but original submissions get 3x the rate of reposts. Check our guidelines and submit your blog soon!


AI Collar That Decodes Dog’s Barks – SwissCognitive

Petpuls Lab Inc. has harnessed to achieve perhaps the first instance of unidirectional interspecies communication made possible through . Through a Fitbit-sized dog collar, the algorithm collects ‘voice’ data of your dog, telling if your best friend is happy, anxious, relaxed, angry or sad. Petpuls’ proprietary algorithm utilizes its database of more than 10,000 bark samples collected from 50 diverse breeds in determining the emotional state via the collar.

SwissCognitive Guest Blogger: Abhinav Raj, a political correspondent for Immigration Advice Services.

What’s in a bark? For starters, data. -powered dog collar from South Korean start-up can analyse a wide range of emotions—from your dog’s barks.