Classifier Boosting with Python

February 03, 2019

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.

Implementation

All boosting methods are located under sklearn.ensemble module. Let's see first how to boost SVM using bagging classifier.

import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import BaggingClassifier
from sklearn.datasets import make_gaussian_quantiles
from sklearn.svm import SVC

# Construct dataset
X1, y1 = make_gaussian_quantiles(cov=2.,
                                 n_samples=200, n_features=2,
                                 n_classes=2, random_state=1)
X2, y2 = make_gaussian_quantiles(mean=(3, 3), cov=1.5,
                                 n_samples=300, n_features=2,
                                 n_classes=2, random_state=1)
X = np.concatenate((X1, X2))
y = np.concatenate((y1, - y2 + 1))

# Create and fit an boosted svm
bdt = BaggingClassifier(SVC())

bdt.fit(X, y)

plot_colors = "br"
plot_step = 0.02
class_names = "AB"

plt.figure(figsize=(10, 5))

# Plot the decision boundaries
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                     np.arange(y_min, y_max, plot_step))

Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
plt.axis("tight")

# Plot the training points
for i, n, c in zip(range(2), class_names, plot_colors):
    idx = np.where(y == i)
    plt.scatter(X[idx, 0], X[idx, 1],
                c=c, cmap=plt.cm.Paired,
                label="Class %s" % n)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.legend(loc='upper right')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Decision Boundary')

Now, let's change the line 18th to AdaBoost and Decision Tree

from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
...
bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), algorithm="SAMME", n_estimators=200)
...

Conclusion

Boosting techniques may dramatically improve the base algorithm and serve an irreplaceable tool in any data scientists toolkit.

Deep Research and Development