Python AdaBoost An Example of How AdaBoost Works

Python AdaBoost An Example of How AdaBoost Works

An Example of How AdaBoost Works

Step 1: A weak classifier (e.g. a decision stump) is made on top of the training data based on the weighted samples. Here, the weights of each sample indicate how important it is to be correctly classified. Initially, for the first stump, we give all the samples equal weights.

Step 2: We create a decision stump for each variable and see how well each stump classifies samples to their target classes. For example, in the diagram below we check for Age, Eating Junk Food, and Exercise. We'd look at how many samples are correctly or incorrectly classified as Fit or Unfit for each individual stump.

Step 3: More weight is assigned to the incorrectly classified samples so that they're classified correctly in the next decision stump. Weight is also assigned to each classifier based on the accuracy of the classifier, which means high accuracy = high weight!

Step 4: Reiterate from Step 2 until all the data points have been correctly classified, or the maximum iteration level has been reached.

Fully grown decision tree (left) vs three decision stumps (right)

Note: Some stumps get more say in the classification than other stumps.


Happy Pythoning.....!!

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