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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Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

What is Machine Learning

The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

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