tut8

100%

✓ Published0🌍 Public

Last edited Feb 13, 2020

Created on Feb 13, 2020

This example shows a machine-learning workflow for predicting Brexit referendum outcomes, comparing predicted versus actual Leave votes in a test dataset. It uses R to fit a final classification model, then applies the `predict` function to generate class predictions on held-out test data. The visualization or summary aspect centers on computing `base_accuracy`, the mean agreement between predicted and observed `Percent.Leave` values. The code relies on base R modeling and prediction APIs, with no plotting libraries, instead focusing on quantitative accuracy assessment rather than graphical output.

AI-generated description

Similar vizzes

Loading thumbnail…

tut8

This visualization shows a decision-tree model of Brexit voting patterns, mapping how various factors predict the percentage of Leave votes across the UK. The code builds a classification tree using the `rpart` function on the `Brexit` dataset, with `Percent.Leave` as the target variable. The tree’s branching structure reveals the key demographic or geographic splits that separate Remain- and Leave-leaning areas. The rendering relies on R’s base graphics to display the tree nodes and splits, while the user must supply a complexity parameter (`cp`) to control tree depth and overfitting.

BBenHeubl

82% match

Loading thumbnail…

tut4

This example shows a decision tree model predicting the percentage of votes to leave the EU, visualized as a tree diagram. It uses the R packages `rpart` and `rpart.plot` to build and render the classification tree from training data. The code sets the complexity parameter to zero via `rpart.control(cp = 0)`, producing a fully grown tree. The plot displays nodes and splits, revealing patterns in the Brexit voting data.

BBenHeubl

79% match

Loading thumbnail…

tut7

This visualization demonstrates the outcome of a classification tree model for predicting Brexit voting behavior. It displays the fitted decision tree structure, showing variable importance and split values. The example uses the R programming language with the `printcp` and `plotcp` functions from the `rpart` package to output the model’s complexity parameters and render its cost-complexity plot. The data originates from a gist by BenHeubl, with the underlying source file containing the model fitting code.

BBenHeubl

77% match

Loading thumbnail…

tut6

This example visualizes the output of a conditional inference tree model that predicts the percentage of “Leave” votes in the Brexit referendum from the training dataset `Brexit_train`. The chart displays both the tree’s internal split nodes—annotated with the chosen split variable and sample size—and terminal nodes, which include a bar plot showing the distribution of the target variable (`Percent.Leave`) as filled proportions. The visualization is built using the `ggparty` and `partykit` R packages, with `ctree()` generating the model and layered `ggplot2`-style geoms (`geom_edge`, `geom_node_label`, `geom_node_plot`) rendering the tree structure and node-level plots.

BBenHeubl

76% match

Loading thumbnail…

Tut3

This visualization demonstrates a machine-learning workflow for splitting a Brexit-related dataset into training and testing subsets. It shows how 70 percent of the data is randomly sampled for model training, with the remaining 30 percent held out for validation. The code uses base R’s `sample()` function and `nrow()` to compute indices, then applies bracket indexing to partition the data frame. The example highlights a common data-preparation step, with the split proportion clearly visible in the sampling logic. No plotting or rendering libraries are used; the focus is purely on the data-splitting mechanism.

BBenHeubl

76% match

Loading thumbnail…

tut5

This example shows a decision tree model of Brexit-related data, visualizing the hierarchical splitting of predictor variables into classification nodes. The tree uses colored boxes with a red-to-yellow-to-green palette to indicate different outcome probabilities or class assignments across branches. The plot is generated with the `rpart.plot` function, using type 4 for a fancier layout, extra 0 for minimal node labeling, and dashed branch lines via `branch.lty = 3`. The underlying model, `Brexit_model`, is built from an rpart decision tree, and the visualization directly renders its structure without additional data processing.

BBenHeubl

75% match

Loading thumbnail…

tut10

This example shows the results of a Boruta feature-selection analysis on a Brexit-related dataset, displaying the importance of each predictor variable in relation to the target variable, Percent.Leave. The visualization uses the Boruta R package to run the algorithm, which compares each feature's importance against shadow attributes to determine if it is important, unimportant, or a tentative holdout. The code calls the `Boruta()` function with the formula and data, then prints the output object to reveal the decision for each attribute. The rendered result is a textual summary of the Boruta run, highlighting which variables are confirmed as significant for the model.

BBenHeubl

68% match

Loading thumbnail…

Tut9

This chart shows the relative importance of variables in a Brexit-related model, ranking them from least to most influential. The code uses R with the ggplot2 library to create a horizontal bar chart, employing `geom_col` with `coord_flip` for orientation. Data is processed through dplyr’s `arrange` and `mutate` functions, with `forcats::fct_inorder` to preserve factor order. The bars are filled in navy blue with white outlines, and the plot uses `theme_bw` for a clean background, displaying the importance scores as bar lengths.

BBenHeubl

65% match