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Workflow Orchestration with Prefect and Coiled - KDnuggets

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Webinar: Workflow Orchestration with Prefect and Coiled When: Jun 30, 2021, 9 am PDT, 12 pm EDT, 17:00 BST. Jeremiah Lowin, Founder and CEO of Prefect, along with Kevin Kho, Prefect's Open Source Community Engineer, will discuss updates about the company and demo a newly released feature called the KV Store. Prefect is an open-source workflow orchestration tool created to handle the modern data stack. Prefect is built on top of Dask, allowing parallel execution of workflows. Coiled helps data scientists use Python for ambitious problems, scaling to the cloud for computing power, ease, and speed--all tuned for the needs of teams and enterprises.


How to plot XGBoost trees in R - Open Source Automation

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In this post, we're going to cover how to plot XGBoost trees in R. XGBoost is a very popular machine learning algorithm, which is frequently used in Kaggle competitions and has many practical use cases. Let's start by loading the packages we'll need. Note that plotting XGBoost trees requires the DiagrammeR package to be installed, so even if you have xgboost installed already, you'll need to make sure you have DiagrammeR also. Next, let's read in our dataset. In this post, we'll be using this customer churn dataset. The label we'll be trying to predict is called "Exited" and is a binary variable with 1 meaning the customer churned (canceled account) vs. 0 meaning the customer did not churn (did not cancel account).


How to use XGBoost algorithm in R in easy steps

#artificialintelligence

Did you know using XGBoost algorithm is one of the popular winning recipe of data science competitions? So, what makes it more powerful than a traditional Random Forest or Neural Network? In the last few years, predictive modeling has become much faster and accurate. I remember spending long hours on feature engineering for improving model by few decimals. A lot of that difficult work, can now be done by using better algorithms.


How to use XGBoost algorithm in R in easy steps

#artificialintelligence

Did you know using XGBoost algorithm is one of the popular winning recipe of data science competitions? So, what makes it more powerful than a traditional Random Forest or Neural Network? In the last few years, predictive modeling has become much faster and accurate. I remember spending long hours on feature engineering for improving model by few decimals. A lot of that difficult work, can now be done by using better algorithms.


Fine-tuning XGBoost in Python like a boss – Towards Data Science

#artificialintelligence

XGBoost (or eXteme Gradient Boosting) is not to be introduced anymore, proved relevant in only too many data science competitions, is still one model that is tricky to fine-tune if you have only been starting playing with it. Because if you have big datasets, and you run a naive grid search on 5 different parameters and having for each of them 5 possible values, then you'll have 5⁵ 3,125 iterations to go. If one iteration takes 10 minutes to run, you'll have more than 21 days to wait before getting your parameters (I don't talk about Python crashing, without letting you know, and you waiting too long before realizing it). I suppose here that you made correctly your job of feature engineering first. Specifically with categorical features, since XGBoost does not take categorical features in input.