Random Forest and R squared meaning

step1: Pick at random K data points from the training set.
step2: Build the Decision Tree associated to these K data points.
step3: Choose the number Ntree of trees you want to build and repeat step1 & step2.
step4: For a new data point, make each one of your Ntree tree predict the value of Y to for the data in question, and assign the new data point the average across all of the predict Y values.

• 1.0 = Perfect fit
• 0.9 = Very good
• <0.7 = Not great
• <0.4 = Terrible
• <0 = Model makes no sense for this data