How Are Decision Trees Constructed In Machine Learning


Great post to look into.

PERPETUAL ENIGMA

1 mainDecision trees occupy an important place in machine learning. They form the basis of Random Forests, which are used extensively in real world systems. A famous example of this is Microsoft Kinect where Random Forests are used to track your body parts. The reason this technique is so popular is because it provides high accuracy with relatively little effort. They are fast to train and they are not computationally expensive. They are not sensitive to outliers either, which helps them to be robust in a variety of cases. So how exactly are they constructed? How are the nodes in the trees generated so that they are optimal?  

View original post 778 more words

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s