- Preparing the data
- Fitting the model and predicting test data
- Accuracy checking
- Source code listing

```
library(e1071)
library(caret)
```

**Preparing the data**

We'll use the Boston housing price dataset as a target regression data in this tutorial. We'll prepare data by splitting it into the train and test parts.

```
boston = MASS::Boston
set.seed(123)
indexes = createDataPartition(boston$medv, p = .9, list = F)
train = boston[indexes, ]
test = boston[-indexes, ]
```

**Fitting the model and predicting test data**

Train and test data are ready. Now, we can define the svm model with default parameters and fit it with train data. Here, we can change the kernel type into 'linear', 'polynomial', and 'sigmoid' for training and predicting. The default is a 'radial' kernel.

```
model_reg = svm(medv~., data=train)
print(model_reg)
Call:
svm(formula = medv ~ ., data = train)
Parameters:
SVM-Type: eps-regression
SVM-Kernel: radial
cost: 1
gamma: 0.07692308
epsilon: 0.1
Number of Support Vectors: 306
```

Next, we'll predict the test data and plot the results to compare visually.

```
pred = predict(model_reg, test)
x = 1:length(test$medv)
plot(x, test$medv, pch=18, col="red")
lines(x, pred, lwd="1", col="blue")
```

**Accuracy checking**

Finally, we'll check the prediction accuracy with the MSE, MAE, RMSE, and R-squared metrics.

```
mse = MSE(test$medv, pred)
mae = MAE(test$medv, pred)
rmse = RMSE(test$medv, pred)
r2 = R2(test$medv, pred, form = "traditional")
cat(" MAE:", mae, "\n", "MSE:", mse, "\n",
"RMSE:", rmse, "\n", "R-squared:", r2)
MAE: 1.877403
MSE: 6.028015
RMSE: 2.455202
R-squared: 0.914078
```

In this tutorial, we have briefly learned how to use an 'e1071' package's svm function for the regression problem. Thank you for reading and the full source code is listed below.

**Source code listing**

```
library(e1071)
library(caret)
# Regression example
boston = MASS::Boston
set.seed(123)
indexes = createDataPartition(boston$medv, p = .9, list = F)
train = boston[indexes, ]
test = boston[-indexes, ]
model_reg = svm(medv~., data=train)
print(model_reg)
pred = predict(model_reg, test)
x=1:length(test$medv)
plot(x, test$medv, pch=18, col="red")
lines(x, pred, lwd="1", col="blue")
# accuracy check
mse = MSE(test$medv, pred)
mae = MAE(test$medv, pred)
rmse = RMSE(test$medv, pred)
r2 = R2(test$medv, pred, form = "traditional")
cat(" MAE:", mae, "\n", "MSE:", mse, "\n",
"RMSE:", rmse, "\n", "R-squared:", r2)
```

` `

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