Decision Trees and Random Forests (Programing language R)

To predict room occupancy using the decision tree classification algorithm.

(a) Load the room occupancy data and train a decision tree classifier. Evaluate the predictive performance by reporting the accuracy obtained on the testing dataset.

(b) Output and analyse the tree learned by the decision tree algorithm, i.e. plot the tree structure and make a discussion about it.

(c) Train a random forests classifier, and evaluate the predictive performance by reporting the accuracy obtained on the testing dataset.

(d) Output and analyse the feature importance obtained by the random forests classifier.

Use the following data:

Testing data:

Temperature,Humidity,Light,CO2,HumidityRatio,Occupancy 21.89,31.55,436.5,1047,0.00512966,No 21.89,31.36,434,1031,0.005098515,No 21.89,31.125,432.75,977.5,0.005059998,No 21.7,28.5,279.3333333,585,0.004576247,Yes 20.6,21.865,454,652.5,0.003274764,No 20.6,22.2,442.75,681.75,0.003325206,No 20.6,22.26,444,702.3333333,0.003334241,No

Training Data:

Temperature,Humidity,Light,CO2,HumidityRatio,Occupancy 23.18,27.272,426,721.25,0.004792988,Yes 23.15,27.2675,429.5,714,0.004783441,Yes 23.15,27.245,426,713.5,0.004779464,Yes 23.15,27.2,426,708.25,0.004771509,Yes 23.1,27.2,426,704.5,0.004756993,Yes 23.1,27.2,419,701,0.004756993,Yes 23.1,27.2,419,701.6666667,0.004756993,Yes 23.1,27.2,419,699,0.004756993,Yes 23.1,27.2,419,689.3333333,0.004756993,Yes 23.075,27.175,419,688,0.004745351,Yes