The Python Bible 7 in 1: Volumes One To Seven (Beginner, Intermediate, Data Science, Machine Learning, Finance, Neural Networks, Computer Vision) by Dedov Florian

Author:Dedov, Florian [Dedov, Florian]
Language: eng
Format: epub
Publisher: UNKNOWN
Published: 2020-03-21T16:00:00+00:00

Since we are not using a line (and thus our model is not linear), we are also preventing mistakes caused by outliers.

Decision Trees

With decision tree classifiers, we construct a decision tree out of our training data and use it to predict the classes of new elements.

This classification algorithm requires very little data preparation and it is also very easy to understand and visualize. On the other hand, it is very easy to be overfitting the model. Here, the model is very closely matched to the training data and thus has worse chances to make a correct prediction on new data.

Random Forest

The last classification algorithm of this chapter is the random forest classifier. It is based on decision trees. What it does is creating a forest of multiple decision trees. To classify a new object, all the various trees determine a class and the most frequent result gets chosen. This makes the result more accurate and it also prevents overfitting. It is also more suited to handle data sets with higher dimensions. On the other hand, since the generation of the forest is random , you have very little control over your model.

Loading Data

Now let us get into the code. In this example, we will get our data directly from the sklearn module. For the program we need the following imports:

import numpy as np

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.