For R, partition the data sets into 60% training and 40% validation and implement the 10-fold cross-validation. Use the statement set. seed(1) to specify the random seed for data partitioning and cross-validation. When searching for the optimal value of k, search within possible k values from 1 to 10. If the predictor variable values are in the character format, then treat the predictor variable as a categorical variable. Otherwise, treat the predictor variable as a numerical variable. Being able to predict machine failures before they happen can save millions of dollars for manufacturing companies. Manufacturers want to be able to perform preventive maintenance or repairs in advance to minimize machine downtime and often install electronic sensors to monitor the machines and their surrounding environment. However, the more sophisticated the machine, the more difficult it is to diagnose and predict the failure rate. Data mining has been used to analyze environmental factors to predict whether or not complex machines such as nanotechnology equipment will fail from one production period to another. The accompanying data set contains 480 observations and three environmental variables: level of humidity in the room where the equipment is located (Humid, in percentage); overall temperature in the room (Temp, in Fahrenheit); and a target variable that indicates whether or not the equipment broke down during the next production period (Breakdown = 1 if breakdown, 0 otherwise).

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Required:
What is the misclassification rate for the optimal k for the training data set?