Evaluate the Model (Optional)
Optionally, you can evaluate the performance of your model using various metrics like accuracy, precision, recall, or F1-score. The tidymodels framework provides functions for model evaluation, making it easy to assess your model’s performance.
R
# Model performance metricsconfusion_matrix <- confusionMatrix(predict(knn_spec), iris$Species)accuracy <- confusion_matrix$overall["Accuracy"]recall <- confusion_matrix$byClass["Recall"]precision <- confusion_matrix$byClass["Precision"]print(confusion_matrix)print(accuracy)print(recall)print(precision) |
Output:
Confusion Matrix and Statistics
Reference
Prediction setosa versicolor virginica
setosa 50 0 0
versicolor 0 50 2
virginica 0 0 48
Overall Statistics
Accuracy : 0.9867
95% CI : (0.9527, 0.9984)
No Information Rate : 0.3333
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.98
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: setosa Class: versicolor Class: virginica
Sensitivity 1.0000 1.0000 0.9600
Specificity 1.0000 0.9800 1.0000
Pos Pred Value 1.0000 0.9615 1.0000
Neg Pred Value 1.0000 1.0000 0.9804
Prevalence 0.3333 0.3333 0.3333
Detection Rate 0.3333 0.3333 0.3200
Detection Prevalence 0.3333 0.3467 0.3200
Balanced Accuracy 1.0000 0.9900 0.9800
Accuracy
0.9866667
[1] NA
[1] NA
- The confusion matrix shows the model’s predictions compared to the actual class labels for three classes: setosa, versicolor, and virginica.
- The overall accuracy of the model is 0.9867, indicating that it correctly classified 98.67% of the instances.
- Recall Measures how well the model correctly identifies each class. For example, it has a sensitivity of 1.0000 for “setosa,” meaning it correctly identifies all “setosa” instances.
In summary, the output provides a comprehensive assessment of the model’s classification performance, including accuracy, precision, recall, and other related statistics for each class in the dataset. The model appears to perform very well, with high accuracy and good class-specific metrics.
Predictions Multiple outcomes with KNN Model Using tidymodels
When dealing with classification problems that involve multiple classes or outcomes, it’s essential to have a reliable method for making predictions. One popular algorithm for such tasks is k-Nearest Neighbors (k-NN). In this tutorial, we will walk you through the process of making predictions with multiple outcomes using a k-NN model in R, specifically with the tidymodels framework.
K-Nearest Neighbors (KNN) is a simple yet effective supervised machine learning algorithm used for classification and regression tasks. Here’s an explanation of KNN and some of its benefits:
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