40 Algorithms Every Programmer Should Know

Using random forest for predictions

Once the model is trained, it can be used to label new data. Each of the individual trees generates a label. The final prediction is determined by voting these individual predictions, as shown:

Note that in the preceding diagram, m trees are trained, which is represented by C₁ to C_m. That is Trees = {C₁,..,C_m}

Each of the trees generates a prediction that is represented by a set:

Individual predictions = P= {P₁,..., P_m}

The final prediction is represented by P_f. It is determined by the majority of the individual predictions. The mode function can be used to find the majority decision (mode is the number ...