Random Forest Classifier

The algorithm gradient boosted regression trees, also called random forests,

belong to the ensemble learning methods This classifier uses an ensemble of

weak regression trees that have a low hit quota when considered in isola-

tion. The quality of the prediction can be improved significantly when vari-

ous trees are trained with different parameters or samples. The results of the

individual trees are aggregated to a total result which then enables a more

balanced and high-quality prediction. The so-called bagging triggered a

boom of the traditional regression trees. As aggregation, either a majority

vote or a probability function is chosen (Fig. 5.5).

The lead prediction generates high-conversion leads because

• The entire spectrum of information available about a company is inte-

grated into the decision-making;

• The data is highly topical and without bias;

• The random forest is capable of abstracting complex correlations in the

data; and

• The method learns iteratively from the interaction with the sales team.

The choice of leads is the first step in the sales process; the second one is to

find the ideal point in time for addressing them.

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