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Gradient Boosting Part1–Visual Conceptualization

Gradient Boosting Part1–Visual Conceptualization

Gradient Boosting Model is a machine learning technique, in league of models like Random forest, Neural Networks etc.

It can be used over regression when there is non-linearity in data, data is sparsely populated, has low fil rate or simply when regression is just unable to give expected results.

Must Read: First step to exploring data: Univariate Analysis

Though GBM is a black box modeling technique with relatively complicated mathematics behind it, this blog aims to present it in a way which helps easy visualization while staying true to the basic nature of the model.

Let us assume a simple classification problem where one has to classify positives and negatives. A simple classification model (with errors associated with it) eg. Regression trees, can be run to acheive it. The Box 1 in the diagram below represents such a model.

Gradient Boosting Part1: Visual Conceptualization

Understanding GBM

Understanding GBM

The following observations can be made from the above diagram:

Box 1: Output of First Weak Learner (From the left)

*Initially all points have same weight (denoted by their size).

  • The decision boundary predicts 2 +ve and 5 -ve points correctly.

Box 2: Output of Second Weak Learner

  • The points classified correctly in box 1 are given a lower weight and vice versa.
  • The model focuses on high weight points now and classifies them correctly. But, others are misclassified now.

Similar trend can be seen in box 3 as well. This continues for many iterations.

In the end, all models (e.g. regression trees) are given a weight depending on their accuracy and a consolidated result is generated.

In a simple notational form if M(x) is our first model say with an 80% accuracy. Instead of building new models altogether, a simpler way would be following

Y= M(x) + error

If the error is white noise i.e it has correlation with the target variable, a model can be built on it

error = G(x) + error2

error2 = H(x) + error3

combining these three:

Y = M(x) + G(x) + H(x) + error3 This probably will have a accuracy of even more than 84%.

Now , optimal weights are given to each of the three learners, Y = alpha * M(x) + beta * G(x) + gamma * H(x) + error4

This was a broad overview just touching the tip of a more complex iceberg behind Gradient Boosting technique. Keep watching the space as we dig some deeper into it.