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 datasetprint(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 statisticssummary(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. Horsepowerggplot(data = mtcars, aes(x = hp, y = mpg, color = factor(am))) + geom_point() + labs(title = "Scatterplot of MPG vs. Horsepower", x = "Horsepower", y = "MPG") |
Output:
R
# Boxplot of MPG by Cylinderggplot(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:
Creating the KNN model
R
# Create a k-NN model specificationknn_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 modelprint(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
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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