### Loading a dataset first

I will be using the *birthwt* dataset which can be found in the *MASS* library:

```
library(MASS)
data(birthwt)
```

It is very important to make sure that any categorical variable is coded as factor first:

```
birthwt$race = as.factor(birthwt$race)
birthwt$smoke = as.factor(birthwt$smoke)
birthwt$ht = as.factor(birthwt$ht)
birthwt$ui = as.factor(birthwt$ui)
```

### Creating a Random Forest algorithm:

```
# Load the randomForest package
library(randomForest)
# Implement the random forest algorithm and look at the result
rf = randomForest(low~., data=birthwt)
rf
```

```
Call:
randomForest(formula = low ~ ., data = birthwt)
Type of random forest: regression
Number of trees: 500
No. of variables tried at each split: 3
Mean of squared residuals: 0.008889174
% Var explained: 95.86
```

We can check variable importance as follows:

```
varImpPlot(rf)
```