Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python by Rudolph Russell

Author:Rudolph Russell [Russell, Rudolph]
Language: eng
Format: epub, pdf
Published: 2018-05-20T18:30:00+00:00

- How to train a random forest classifier using the forest function in Scikit-Learn.

- Understanding Multi-Output Classification.

- Understanding multi-Label classifications.

REFERENCES

http://scikit-learn.org/stable/install.html

https://www.python.org

https://matplotlib.org/2.1.0/users/installing.html

http://yann.lecun.com/exdb/mnist/

CHAPTER 3

HOW TO TRAIN A MODEL

After working with many machine learning models and training algorithms, which seem like unfathomable black boxes. we were able to optimize a regression system, have also worked with image classifiers. But we developed these systems without understanding what's s inside and how they work, so now we need to go deeper so that we can grasp how they work and understand the details of implementation.

Gaining a deep understanding of these details will help you with the right model and with choosing the best training algorithm. Also, it will help you with debugging and error analysis.

In this chapter, we'll work with polynomial regression, which is a complex model that works for nonlinear data sets. In addition, we'll working with several regularization techniques that reduce training that encourages overfitting.

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