You can also watch this video which goes through the following example step-by-step:

After installing the OneR package from CRAN load it

library(OneR)

Use the famous Iris dataset and determine optimal bins for numeric data

data <- optbin(iris)

Build model with best predictor

model <- OneR(data, verbose = TRUE) ## ## Attribute Accuracy ## 1 * Petal.Width 96% ## 2 Petal.Length 95.33% ## 3 Sepal.Length 74.67% ## 4 Sepal.Width 55.33% ## --- ## Chosen attribute due to accuracy ## and ties method (if applicable): '*'

Show learned rules and model diagnostics

summary(model) ## ## Call: ## OneR(data = data, verbose = TRUE) ## ## Rules: ## If Petal.Width = (0.0976,0.791] then Species = setosa ## If Petal.Width = (0.791,1.63] then Species = versicolor ## If Petal.Width = (1.63,2.5] then Species = virginica ## ## Accuracy: ## 144 of 150 instances classified correctly (96%) ## ## Contingency table: ## Petal.Width ## Species (0.0976,0.791] (0.791,1.63] (1.63,2.5] Sum ## setosa * 50 0 0 50 ## versicolor 0 * 48 2 50 ## virginica 0 4 * 46 50 ## Sum 50 52 48 150 ## --- ## Maximum in each column: '*' ## ## Pearson's Chi-squared test: ## X-squared = 266.35, df = 4, p-value < 2.2e-16

Plot model diagnostics

plot(model)

Use model to predict data

prediction <- predict(model, data)

Evaluate prediction statistics

eval_model(prediction, data) ## ## Confusion matrix (absolute): ## Actual ## Prediction setosa versicolor virginica Sum ## setosa 50 0 0 50 ## versicolor 0 48 4 52 ## virginica 0 2 46 48 ## Sum 50 50 50 150 ## ## Confusion matrix (relative): ## Actual ## Prediction setosa versicolor virginica Sum ## setosa 0.33 0.00 0.00 0.33 ## versicolor 0.00 0.32 0.03 0.35 ## virginica 0.00 0.01 0.31 0.32 ## Sum 0.33 0.33 0.33 1.00 ## ## Accuracy: ## 0.96 (144/150) ## ## Error rate: ## 0.04 (6/150) ## ## Error rate reduction (vs. base rate): ## 0.94 (p-value < 2.2e-16)

Please note that the very good accuracy of 96% is reached effortlessly.

“Petal.Width” is identified as the attribute with the highest predictive value. The cut points of the intervals are found automatically (via the included optbin function). The results are three very simple, yet accurate, rules to predict the respective species.

The nearly perfect separation of the areas in the diagnostic plot give a good indication of the model’s ability to separate the different species.

The whole code of this post:

library(OneR) data <- optbin(iris) model <- OneR(data, verbose = TRUE) summary(model) plot(model) prediction <- predict(model, data) eval_model(prediction, data)

More sophisticated examples will follow in upcoming posts… so stay tuned!

**Update:** For more examples see e.g. this post: OneR – Fascinating Insights through Simple Rules

## Help

From within R:

help(package = OneR)

…or as a pdf here: OneR.pdf

The package vignette: OneR – Establishing a New Baseline for Machine Learning Classification Models

Issues can be posted here: https://github.com/vonjd/OneR/issues

## Feedback

I would love to hear about your experiences with the OneR package. Please drop a line or two in the comments – Thank you!