ADABOOST
ALGORITHM
By Rezhna , Dilon , Abdulla
Outlines
Introduction
Weak Learners
Boost Process
Applications of AdaBoost
Conclusion
Introduction
AdaBoost (short for Adaptive Boosting) is
a popular machine learning algorithm used
for improving the accuracy of binary
classification models. It was first
introduced in 1995 by Yoav Freund and
Robert Schapire. The key idea behind
AdaBoost is to combine multiple weak
classifiers, which are simple classifiers that
are only slightly better than random
guessing, into a strong classifier.
Introduction
AdaBoost has become a popular
algorithm due to its ability to
effectively handle high-dimensional
data, handle non-linear data, and its
strong performance in many practical
applications. It has been successfully
applied in a variety of areas, including
computer vision, natural language
processing, and finance
Weak Learners
Weak learners are often used in
ensemble methods, such as
AdaBoost, because they can be
combined to create a stronger
model. The idea is that by
combining many weak learners,
each of which has a small amount
of predictive power, we can
create a strong learner with high
accuracy.
Weak Learners
Some examples of weak
learners include, which are
decision trees with only one
split, and linear classifiers,
which can only classify data
based on a linear boundary.
These models are simple and
easy to train, but they may not
be accurate enough to be used
on their own.
Weak Learners
In the AdaBoost algorithm, weak learners are trained on a
weighted version of the data, where the weights are adjusted
in each iteration to focus more on the misclassified samples.
This allows the algorithm to iteratively create a strong learner
from a collection of weak learners.
Boost Process
Boosting is a technique that
iteratively combines many
weak learners into a strong
learner. It focuses on
misclassified samples to
improve accuracy. AdaBoost
is a popular example of a
boosting algorithm.
Applications
1.
2.
Object Detection: AdaBoost has been used in object detection
tasks, such as detecting faces or cars in images. The algorithm
can be used to train classifiers that can detect objects in images
by combining many weak classifiers.
Sentiment Analysis: AdaBoost has also been used in sentiment
analysis, where the goal is to predict the sentiment of a text,
such as whether a movie review is positive or negative.
AdaBoost can be used to train classifiers that can accurately
predict the sentiment of text by combining many weak
classifiers.
Conclusion
AdaBoost is a powerful and versatile boosting algorithm that
combines many weak classifiers into a strong classifier,
improving the accuracy of machine learning models.