Deep Research and Development

Introduction Remember we've talked about random forest and how it was used to improve the performance of a single Decision Tree classifier . The idea of fitting a number of decision tree classifiers on various sub-samples of the dataset and using averaging to improve the predictive accuracy can be used to other algorithms as well and it's called boosting. There are several boosting techniques, which can be used to improve our algorithm, we'll cover the most used ones: AdaBoost and Bagging boost. AdaBoost An AdaBoost classifier begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset, but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases. Bagging boost A Bagging classifier fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions to form a final prediction. I...

Introduction Last time we talked about non-linear classifier using Support Vector Machines or SVM . Today we'll be discussing another non-linear classifier and regressor called decision tree. The way decision tree works is by creating a model, which predicts the value of a target variable by learning simple decision rules inferred from the data features. Since trees can be visualized and is something we're all used to, decision trees can easily be explained, visualized and manipulated the non-linearity in an intuitive manner. Surely there are some disadvantages as well, but we'll note them a bit later, firstly let's see them in action. Implementation It won't come as a complete surprise to you, that scikit package has already taken initiative and implemented the whole thing using DecisionTreeRegressor and DecisionTreeClassifier classes. What is left for us is to bear the fruits of someone else's hard labour. import StringIO import numpy as np im...