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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:

K-Nearest Neighbors (KNN):

KNN is a non-parametric algorithm, meaning it doesn’t make any underlying assumptions about the distribution of data. It’s an instance-based or memory-based learning algorithm, which means it memorizes the entire training dataset and uses it to make predictions. The fundamental idea behind KNN is to classify a new data point by considering the majority class among its K-nearest neighbors.

How KNN Works:

  • Distance Metric: KNN uses a distance metric (commonly Euclidean distance) to measure the similarity between data points. The distance is calculated in the feature space, where each feature represents a dimension.
  • Choosing K: You need to specify the value of K, which determines the number of nearest neighbors to consider when making predictions. A small K can lead to a noisy model, while a large K can make the decision boundary overly smooth.
  • Prediction: To classify a new data point, KNN finds the K training data points that are closest to the new point based on the chosen distance metric. It then assigns the class that is most frequent among these K neighbors to the new point.

Benefits of KNN:

  • Simplicity: KNN is easy to understand and implement. It doesn’t involve complex mathematical equations or hyperparameter tuning.
  • Versatility: KNN can be used for both classification and regression tasks. For classification, it assigns a class label, and for regression, it predicts a numerical value based on the average or weighted average of the nearest neighbors.
  • No Assumptions: KNN makes no assumptions about the underlying data distribution, which makes it suitable for various types of datasets, including those with complex or non-linear relationships.
  • Robustness: KNN is robust to noisy data because it relies on a voting mechanism that considers multiple neighbors. Outliers can have a limited impact on the overall prediction.
  • Interpretability: KNN provides transparency in predictions, as you can easily trace back the reasoning behind a classification decision by examining the nearest neighbors.
  • Non-parametric: Being non-parametric, KNN can capture complex decision boundaries, making it suitable for datasets with intricate structures.

However, KNN also has some limitations. It can be computationally expensive for large datasets, and the choice of the distance metric and K value can significantly impact its performance. Additionally, it doesn’t provide insights into feature importance, which can be essential in some applications.

Tidymodels

Tidymodels is a powerful and user-friendly ecosystem for modeling and machine learning in R. It provides a structured workflow for creating, tuning, and evaluating models. Before we proceed, make sure you have tidymodels and the necessary packages installed. You can install them using:

R

install.packages("tidymodels")

Now, let’s dive into the steps to get predictions with multiple outcomes using a k-NN model.

Pre-Requisites

Before moving forward make sure you have Caret and ggplot packages installed.

R

install.packages('caret')
install.packages('ggplot2')

  • Caret – caret package enables you to train different types of algorithms using a simple train function
  • ggplot2 – ggplot2 is a R package dedicated to data visualization

Load Required Libraries and Data

We’ll start by loading the necessary libraries and a dataset. For this tutorial, we’ll use the classic Iris dataset, which contains three different species of iris flowers (setosa, versicolor, and virginica).

R

library(caret)
 
 
# ggplot is used for plotting
library(ggplot2) 
library(tidymodels)
 
# Load the Iris dataset
data(iris)

Preprocess Data

Data preprocessing is crucial for building a robust model. In this step, we’ll create a recipe to preprocess the data. In our case, we don’t need any preprocessing since the Iris dataset is well-structured and doesn’t have any missing values.

R

# Create a recipe for data preprocessing
preprocess_recipe <- recipe(Species ~ ., data = iris)

Create and Train the k-NN Model

Now, it’s time to create and train our k-NN model. We’ll use the `nearest_neighbor()` function from the `parsnip` package, which is part of tidymodels.

R

# Create a k-NN model specification
knn_spec <- train(
  Species ~ .,
  data = iris,
  method = "kknn",
  trControl = trainControl(method = "cv", number = 5, verboseIter = TRUE),
  tuneLength = 5
)

Output:

+ Fold1: kmax= 5, distance=2, kernel=optimal 
- Fold1: kmax= 5, distance=2, kernel=optimal 
+ Fold1: kmax= 7, distance=2, kernel=optimal 
- Fold1: kmax= 7, distance=2, kernel=optimal 
+ Fold1: kmax= 9, distance=2, kernel=optimal 
- Fold1: kmax= 9, distance=2, kernel=optimal 
+ Fold1: kmax=11, distance=2, kernel=optimal 
- Fold1: kmax=11, distance=2, kernel=optimal 
+ Fold1: kmax=13, distance=2, kernel=optimal 
- Fold1: kmax=13, distance=2, kernel=optimal 
+ Fold2: kmax= 5, distance=2, kernel=optimal 
- Fold2: kmax= 5, distance=2, kernel=optimal 
+ Fold2: kmax= 7, distance=2, kernel=optimal 
- Fold2: kmax= 7, distance=2, kernel=optimal 
+ Fold2: kmax= 9, distance=2, kernel=optimal 
- Fold2: kmax= 9, distance=2, kernel=optimal 
+ Fold2: kmax=11, distance=2, kernel=optimal 
- Fold2: kmax=11, distance=2, kernel=optimal 
+ Fold2: kmax=13, distance=2, kernel=optimal 
- Fold2: kmax=13, distance=2, kernel=optimal 
+ Fold3: kmax= 5, distance=2, kernel=optimal 
- Fold3: kmax= 5, distance=2, kernel=optimal 
+ Fold3: kmax= 7, distance=2, kernel=optimal 
- Fold3: kmax= 7, distance=2, kernel=optimal 
+ Fold3: kmax= 9, distance=2, kernel=optimal 
- Fold3: kmax= 9, distance=2, kernel=optimal 
+ Fold3: kmax=11, distance=2, kernel=optimal 
- Fold3: kmax=11, distance=2, kernel=optimal 
+ Fold3: kmax=13, distance=2, kernel=optimal 
- Fold3: kmax=13, distance=2, kernel=optimal 
+ Fold4: kmax= 5, distance=2, kernel=optimal 
- Fold4: kmax= 5, distance=2, kernel=optimal 
+ Fold4: kmax= 7, distance=2, kernel=optimal 
- Fold4: kmax= 7, distance=2, kernel=optimal 
+ Fold4: kmax= 9, distance=2, kernel=optimal 
- Fold4: kmax= 9, distance=2, kernel=optimal 
+ Fold4: kmax=11, distance=2, kernel=optimal 
- Fold4: kmax=11, distance=2, kernel=optimal 
+ Fold4: kmax=13, distance=2, kernel=optimal 
- Fold4: kmax=13, distance=2, kernel=optimal 
+ Fold5: kmax= 5, distance=2, kernel=optimal 
- Fold5: kmax= 5, distance=2, kernel=optimal 
+ Fold5: kmax= 7, distance=2, kernel=optimal 
- Fold5: kmax= 7, distance=2, kernel=optimal 
+ Fold5: kmax= 9, distance=2, kernel=optimal 
- Fold5: kmax= 9, distance=2, kernel=optimal 
+ Fold5: kmax=11, distance=2, kernel=optimal 
- Fold5: kmax=11, distance=2, kernel=optimal 
+ Fold5: kmax=13, distance=2, kernel=optimal 
- Fold5: kmax=13, distance=2, kernel=optimal 
Aggregating results
Selecting tuning parameters
Fitting kmax = 9, distance = 2, kernel = optimal on full training set

R

# Print the model
print(knn_spec)

Output:

k-Nearest Neighbors 
150 samples
  4 predictor
  3 classes: 'setosa', 'versicolor', 'virginica' 
No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 120, 120, 120, 120, 120 
Resampling results across tuning parameters:
  kmax  Accuracy   Kappa
   5    0.9466667  0.92 
   7    0.9533333  0.93 
   9    0.9533333  0.93 
  11    0.9466667  0.92 
  13    0.9466667  0.92 
Tuning parameter 'distance' was held constant at a value of 2
Tuning
 parameter 'kernel' was held constant at a value of optimal
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were kmax = 9, distance = 2 and kernel
 = optimal.
  • knn_spec <- train(…): This line creates a k-NN (k-nearest neighbors) model specification using the `train` function from the `caret` package. The `train` function is used for training various machine learning models.
  • Species ~ .: This formula specifies the target variable (Species) and the predictors (all other columns denoted by `.`) to be used in the model.
  • data = iris: This specifies the dataset to be used, in this case, the Iris dataset loaded using `data(iris)`.
  • method = “kknn”: Here, we specify that we want to use the “kknn” model, which stands for kernel k-nearest neighbors. This is a variation of the k-NN algorithm that uses kernel density estimation to make predictions.
  • trControl = trainControl(…): This part sets the control parameters for the training process. It specifies that we want to perform cross-validation (`method = “cv”`) with 5 folds (`number = 5`) and requests verbose output during the training process (`verboseIter = TRUE`).
  • tuneLength = 5: This parameter specifies the number of neighbors (`k`) to try during cross-validation. In this case, we are trying five different values of `k` to determine which one provides the best model performance.
  • print(knn_spec): Finally, we print the k-NN model specification to the console. This provides information about the model, including the method used, the tuning parameters, and other details.

This code sets up a k-NN classification model using the “kknn” method, performs cross-validation with different values of `k`, and prints information about the model specification. It’s a common practice in machine learning to explore different hyperparameters (like `k` in k-NN) to find the best model for a given problem. The resulting `knn_fit` will contain the trained and tuned k-NN model.

Make Predictions

With our trained k-NN model, we can now make predictions. We’ll use the `predict()` function to predict the species of iris flowers.

R

# Make predictions on the test data
predictions <- predict(knn_spec, new_data = iris)
print(predictions)

Output:

[1] setosa     setosa     setosa     setosa     setosa     setosa    
  [7] setosa     setosa     setosa     setosa     setosa     setosa    
 [13] setosa     setosa     setosa     setosa     setosa     setosa    
 [19] setosa     setosa     setosa     setosa     setosa     setosa    
 [25] setosa     setosa     setosa     setosa     setosa     setosa    
 [31] setosa     setosa     setosa     setosa     setosa     setosa    
 [37] setosa     setosa     setosa     setosa     setosa     setosa    
 [43] setosa     setosa     setosa     setosa     setosa     setosa    
 [49] setosa     setosa     versicolor versicolor versicolor versicolor
 [55] versicolor versicolor versicolor versicolor versicolor versicolor
 [61] versicolor versicolor versicolor versicolor versicolor versicolor
 [67] versicolor versicolor versicolor versicolor versicolor versicolor
 [73] versicolor versicolor versicolor versicolor versicolor versicolor
 [79] versicolor versicolor versicolor versicolor versicolor versicolor
 [85] versicolor versicolor versicolor versicolor versicolor versicolor
 [91] versicolor versicolor versicolor versicolor versicolor versicolor
 [97] versicolor versicolor versicolor versicolor virginica  virginica 
[103] virginica  virginica  virginica  virginica  versicolor virginica 
[109] virginica  virginica  virginica  virginica  virginica  virginica 
[115] virginica  virginica  virginica  virginica  virginica  versicolor
[121] virginica  virginica  virginica  virginica  virginica  virginica 
[127] virginica  virginica  virginica  virginica  virginica  virginica 
[133] virginica  virginica  virginica  virginica  virginica  virginica 
[139] virginica  virginica  virginica  virginica  virginica  virginica 
[145] virginica  virginica  virginica  virginica  virginica  virginica 
Levels: setosa versicolor virginica

The `predictions` object now contains the predicted species for each observation in the dataset.

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 metrics
confusion_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.

Performing KNN on MTCars Dataset

Performing simple EDA on the “mtcars” dataset, using head and summary functions:

R

library(caret)
library(ggplot2) 
 
# Load the mtcars dataset (built-in dataset)
data(mtcars)
 
# EDA (Exploratory Data Analysis)
# Let's take a quick look at the dataset
print(mtcars)

Output:

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Summary of the Dataset

R

# Summary statistics
summary(mtcars)

Output:

     mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000

Creating visualizations to interpret the dataset

R

# Visualizations
# Scatterplot of MPG vs. Horsepower
ggplot(data = mtcars, aes(x = hp, y = mpg, color = factor(am))) +
  geom_point() +
  labs(title = "Scatterplot of MPG vs. Horsepower", x = "Horsepower", y = "MPG")

Output:

Rplot

R

# Boxplot of MPG by Cylinder
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
  geom_boxplot() +
  labs(title = "Boxplot of MPG by Cylinder", x = "Cylinder", y = "MPG") +
  scale_fill_manual(values = c("red", "green", "blue")) 
# Optional: Change fill colors

Output:

Rplot01

Creating the KNN model

R

# Create a k-NN model specification
knn_spec <- train(
  am ~ ., 
  data = mtcars,
  method = "knn", 
  trControl = trainControl(method = "cv", number = 5, verboseIter = TRUE),
  tuneLength = 5 
)

Output:

+ Fold1: k= 5 
- Fold1: k= 5 
+ Fold1: k= 7 
- Fold1: k= 7 
+ Fold1: k= 9 
- Fold1: k= 9 
+ Fold1: k=11 
- Fold1: k=11 
+ Fold1: k=13 
- Fold1: k=13 
+ Fold2: k= 5 
- Fold2: k= 5 
+ Fold2: k= 7 
- Fold2: k= 7 
+ Fold2: k= 9 
- Fold2: k= 9 
+ Fold2: k=11 
- Fold2: k=11 
+ Fold2: k=13 
- Fold2: k=13 
+ Fold3: k= 5 
- Fold3: k= 5 
+ Fold3: k= 7 
- Fold3: k= 7 
+ Fold3: k= 9 
- Fold3: k= 9 
+ Fold3: k=11 
- Fold3: k=11 
+ Fold3: k=13 
- Fold3: k=13 
+ Fold4: k= 5 
- Fold4: k= 5 
+ Fold4: k= 7 
- Fold4: k= 7 
+ Fold4: k= 9 
- Fold4: k= 9 
+ Fold4: k=11 
- Fold4: k=11 
+ Fold4: k=13 
- Fold4: k=13 
+ Fold5: k= 5 
- Fold5: k= 5 
+ Fold5: k= 7 
- Fold5: k= 7 
+ Fold5: k= 9 
- Fold5: k= 9 
+ Fold5: k=11 
- Fold5: k=11 
+ Fold5: k=13 
- Fold5: k=13 
Aggregating results
Selecting tuning parameters
Fitting k = 5 on full training set

R

# Print the model
print(knn_spec)

Output:

k-Nearest Neighbors 
32 samples
10 predictors
No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 26, 25, 26, 26, 25 
Resampling results across tuning parameters:
  k   RMSE       Rsquared   MAE      
   5  0.4292704  0.4401123  0.3019048
   7  0.4089749  0.5099996  0.3054422
   9  0.4203775  0.5427578  0.3333333
  11  0.4267676  0.5400401  0.3501443
  13  0.4357731  0.5447669  0.3782051
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was k = 7.

The code performs the following steps:

  • Model Specification: It specifies a k-Nearest Neighbors (k-NN) classification model using the train function from the caret package. The model aims to predict the binary variable am (automatic transmission: 0 or 1) based on all other variables in the mtcars dataset. The method chosen for classification is “knn.”
  • Cross-Validation: It sets up the cross-validation procedure using the trainControl function. In this case, it uses 5-fold cross-validation (number = 5) and provides some additional information during the training process with verboseIter = TRUE.
  • Hyperparameter Tuning: It specifies that hyperparameter tuning should be performed with tuneLength = 5, which means that it will try different values of the k parameter (number of neighbors) to find the best one.
  • Printing the Model: It prints out the details of the k-NN model specification.
  • Model Evaluation: It evaluates the performance of the k-NN model using RMSE, Required, MAE

Conclusion

In this tutorial, we’ve shown you how to make predictions with multiple outcomes using a k-NN model in tidymodels. The key steps involve loading data, preprocessing it, creating and training the model, making predictions, and optionally evaluating the model’s performance.

Tidymodels simplifies the entire process, allowing you to focus on building and assessing models rather than dealing with low-level details. It’s a valuable tool for anyone working on classification tasks with multiple outcomes.


 


Reffered: https://www.geeksforgeeks.org

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