Publications by Business Science
Hyperparameter Tuning Forecasts in Parallel with Modeltime
Speed up forecasting with modeltime’s new built-in parallel processing. Fitting many time series models can be an expensive process. To help speed up computation, modeltime now includes parallel processing, which is support for high-performance computing by spreading the model fitting steps across multiple CPUs or clusters. Highlights We now ...
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plotnine: Make great-looking correlation plots in Python
The plotnine library is a powerful python visualization library based on R’s ggplot2 package. In this tutorial, we show you how to make a great-looking correlation plot using pandas and plotnine. This article is part of Python-Tips Weekly, a bi-weekly video tutorial that shows you step-by-step how to do common Python coding tasks. Here are th...
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Gentle Introduction to Forecasting with Modeltime [Video Tutorial]
A gentle introduction to our forecasting package, modeltime. Modeltime extends the Tidymodels ecosystem for time series forecasting. Learn how to forecast with ARIMA, Prophet, and linear regression time series models in this short video tutorial. This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do...
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R is for Research, Python is for Production
👉 Sign Up For More Blog Articles 👈 Updated July 2021 Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem. R is for Research If I had to describe R in one word, it would be: tidyverse. It has made research tasks – ...
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easystats: Quickly investigate model performance
Easystats performance is an R package that makes it easy to investigate the relevant assumptions for regression models. Simply use the check_model() function to produce a visualization that combines 6 tests for model performance. We’ll quickly: Learn how to investigate performance with performance::check_model() Check out the Tidymodels integr...
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Forecasting Many Time Series (Using NO For-Loops)
Spending too much time on making iterative forecasts? I’m super excited to introduce the new panel data forecasting functionality in modeltime. It’s perfect for forecasting many time series at once without for-loops saving you time ⏱️ and aggravation ?. Just say NO to for-loops for forecasting. Fitting many time series can be an expensi...
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ggdist: Make a Raincloud Plot to Visualize Distribution in ggplot2
The ggdist package is a ggplot2 extension that is made for visualizing distributions and uncertainty. We’ll show see how ggdist can be used to make a raincloud plot. R-Tips Weekly This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Here are the links to get set up. ? Get t...
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ggforce: Make a Hull Plot to Visualize Clusters in ggplot2
The ggforce package is a ggplot2 extension that adds many exploratory data analysis features. In this tutorial, we’ll learn how to make hull plots for visualizing clusters or groups within our data. R-Tips Weekly This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Here are ...
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ggalt: Make a Dumbbell Plot to Visualize Change in ggplot2
ggalt is a ggplot2 extension that adds many new ggplot geometries. In this tutorial, we’ll learn how to make dumbbell plots for visualizing change within our data using geom_dumbbell(). R-Tips Weekly This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Here are the links to ...
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ggalt: Make a Lollipop Plot to Compare Categories in ggplot2
A Lollipop Plot shows the relationship between categories using a dot and a line that connects to a baseline (similar to a Bar Plot). In this short tutorial, we use ggalt to create a Lollipop Plot with the geom_lollipop() function. R-Tips Weekly This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do c...
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