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Explore orca encounters using Positron, a next-generation data science IDE, in this hands-on demonstration of its capabilities and features.
Dive into R package development with Positron, a next-generation data science IDE, as Julia Silge demonstrates the process of releasing a package to CRAN.
Discover how to leverage GitHub Copilot integration with Positron Assistant for enhanced AI-powered data science coding workflows in this 16-minute tutorial.
Discover Positron's enhanced Data Explorer through literary prize analysis, featuring interactive data viewing for CSV, Parquet files, and R/Python objects in this monthly release demo.
Handle high-cardinality predictors in tidymodels using likelihood or effect encodings, then build and evaluate an XGBoost model with UK museum data.
Learn to expose a text classification model through REST endpoints that return predicted classes or probabilities.
Package a LEGO-set prediction model in Docker and expose it through a portable prediction API.
Explore three increasingly robust ways to analyze and visualize UK gender pay-gap data across economic activities: summaries, linear modeling, and bootstrap intervals.
Use R and tidymodels to interpret linear-model coefficients and uncertainty intervals while analyzing collegiate sports expenditures.
Fit a zero-inflated Poisson model in R with tidymodels to analyze package vignette counts using special formulas and bootstrap resampling.
Use custom feature engineering, tuned XGBoost, and SHAP-based plots to predict board-game ratings and explain nonlinear model behavior.
Use R to prepare lyrics, train an unsupervised topic model, and explore the topics discovered in Spice Girls songs.
Use tidymodels workflows and bootstrap resampling to extract model coefficients while predicting Doctor Who episode viewership from dates.
Use tidymodels workflowsets to compare preprocessing and modeling combinations for predicting giant pumpkin weights.
Build, tune, and evaluate a multiclass lasso model using working-paper titles and publication years to predict economics research categories.
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