tut21

100%

✓ Published0🌍 Public

Last edited Feb 13, 2020

Created on Feb 13, 2020

This example shows a supervised machine learning workflow using the random forest algorithm to predict income categories from a dataset of demographic features. The code builds a random forest model with 500 trees and three variables considered at each split, using the `randomForest` function from the R randomForest package. After training on a designated training set, it generates predictions and compares them against actual income labels using a confusion matrix, reporting a classification accuracy of approximately 77.6%. The visualization itself is not graphical; instead, it presents the model’s performance metrics as tabular and numeric output.

AI-generated description

Similar vizzes

Loading thumbnail…

tut14

This example visualizes the relative importance of different variables in predicting income levels using a random forest model. The code loads an income dataset from GitHub, shuffles rows, and handles missing factor values before training the model with `randomForest`. It then relies on `ggRandomForests` to plot variable importance scores, showing which predictors most strongly influence income classification. The visual output highlights the ranked contribution of each feature, with the model trained on the processed data frame.

BBenHeubl

83% match

Loading thumbnail…

tut19

This visualization demonstrates the accuracy of a decision tree model in predicting the `INCOME` category from a training dataset. It shows a confusion matrix and the computed accuracy rate of approximately 73.4%, comparing predicted versus actual income labels. The R code uses the `caret` package’s `train()` function with the `"rpart"` method to build the model, then evaluates it with the `table()` and `mean()` functions. The script also loads `e1071`, though the visible steps primarily rely on caret and rpart for classification and validation.

BBenHeubl

78% match

Loading thumbnail…

tut17

This example builds a classification-ready dataset from income survey data, showing how raw income brackets are recoded into binary “Under 30k” versus “Over 30k” categories. It then splits the cleaned data into training and test sets using base R’s `sample` function. The code uses `dplyr`’s `mutate`, `if_else`, and `mutate_if` for transformations, along with `forcats::fct_explicit_na` to label missing factors as “Unknown.” The data is sourced from a remote CSV via `read.csv` and processed entirely in R, with no visualization rendering or plotting involved.

BBenHeubl

73% match

Loading thumbnail…

tut18

This visualization shows how model accuracy varies as the `mtry` parameter changes from 3 to 8 in a random forest classifier. A line plot displays the accuracy scores, which peak at approximately 0.645. The code uses a `for` loop to train random forest models with the `randomForest` R package on a training set, validates predictions against a validation set, and records mean accuracy. The final `plot(3:8, accuracy_list)` call generates the line chart from the computed accuracy vector.

BBenHeubl

65% match

Loading thumbnail…

tut8

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.

BBenHeubl

65% 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

65% 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

64% 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

64% match