Image classification using cascaded boosting in scikit-learn - why would classification terminate early?

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I've pasted all my code here in case you'd need that to understand my question: Plotting a graph on axes but getting no results while trying to classify image based on HoG features

My question is: given approximately 500 images (the Caltech cars dataset) with 48 features each, what possible reasons can there be for the boosting to terminate early? What could a perfect fit, or a problem with the boosted sample weights, and how can such problems be solved? The specific algorithm I'm using is SAMME, a multiclass Adaboost classifier. I'm using Python 2.7 on Anaconda.

When I checked certain variables during the classification of my dataset, setting the n_estimators parameter to be 600, I found that:

• discrete_test_errors: consisted of 1 item instead of being an array of 600 values


• discrete_estimator_errors: was again one single value instead of of being an array of 600 values


• real_test_errors is just one item again instead of 600


• discrete_estimator_weights: array ([1.]) "


• n_trees_discrete and n_trees_real: 1 instead of 600

asked 37 secs ago

user961627

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