Machine Learning: An Introduction to Supervised and Unsupervised Learning Algorithms by Michael Colins

Author:Michael Colins
Language: eng
Format: azw3
Published: 2017-07-19T07:00:00+00:00

The last function takes after using the average, multiTest(), and running the function colicTest() 10 times. After running test script with some input data, the analysis might look like this.

The data had a 35% error rate after 10 iterations. This was not bad having 30% of the values missing. To get results approaching a 20% error rate, you can alter the alpha size in stochGradAscent1() and also the number of iterations in colicTest().

Summary

● Logistic regression means finding the best-fit parameters to a nonlinear function that is termed as the sigmoid.

● One of the most common optimization algorithms is the gradient ascent.

● Finding the best-fit parameters requires methods of optimization.

● Stochastic gradient ascent simplifies the Gradient.

● Stochastic gradient ascent uses fewer computing resources compared to gradient ascent. Stochastic gradient ascent is an online algorithm that does not load data like batch processing, instead, it can update what it has learned while new data comes in.

● How to deal with missing values in the data is a major problem in machine learning. There is no overall answer to this question. It depends on your intention with the data. There are some solutions, and each solution has its pros and cons.

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.