Introduction to Module 6

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来自 University of Illinois at Urbana-Champaign 的课程

Data Analytics Foundations for Accountancy II

3 个评分

University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

3 个评分

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

从本节课中

Module 6: Feature Engineering

This module introduces an important concept in machine learning, the selection of the actual features that will be used by a machine learning algorithm. Along with data cleaning, this step in the data analytics process is extremely important, yet it is often overlooked as a method for improving the overall performance of an analysis. This module beings with a discussion of ethics in machine learning, in large part because the selection of features can have (sometimes) non-obvious impacts on the final performance of an algorithm. This can be important when machine learning is applied to data in a regulated industry or when the improper application of an algorithm might lead to discrimination. The rest of this module introduces different techniques for either selecting the best features in a data set, or the construction of new features from the existing set of features.

Introduction to Module 65:03

与讲师见面

Robert Brunner
Professor
Accountancy

Hello. We were in the atrium of the NCSA building,

where new features of both hardware and software are explored and evaluated in

order to improve the efficacy of high performance computing and big data analysis.

Previous work like this at NCSA resulted in the early web browsers and web servers.

A supercomputer made from video game consoles,

an immersive CAVE Visualization Environment,

and a specialized GIS computational platform.

Now, NCSA is increasingly exploring data intensive hardware and

software to aid scientists around

the world as they struggle with ever increasing amounts of data.

In this module, we look to improve the performance of

machine learning algorithms by employing feature engineering.

Feature engineering encompasses any aspect of

selecting or creating the features used to generate a machine learning model.

Often this task is overlooked in favor of focusing

on trying out the latest classification or regression algorithm.

However, time spent on feature engineering often pays back amazing dividends.

In part, this is because any machine learning algorithm will work faster,

and be easier to understand if the most important features are used to make predictions.

And this model often performs nearly as well as and

sometimes even better than a model generated from more data.

One overlooked aspect of feature engineering is

the selection of features based on ethical or moral concerns.

This can be especially important and regulated for public industries.

For example, if you were determined credit ratings or university admissions,

you would not want to base a decision on ethnicity or gender.

While it might be easy to exclude those features from an analysis,

how do you ensure there are not hidden correlations between

these features and other features that remain in the data set,

such as home address or occupation.

To explore these issues in more detail the first lesson includes

articles on the ethics of machine learning and artificial intelligence.

As automation becomes more prevalent in our society,

this issue will become more important for

both individuals and companies who wish to behave ethically,

and to mitigate potential liabilities.

The next lesson introduces feature selection where algorithms determine

the set of features that optimize the performance of a given machine learning algorithm.

There are a number of such methods some of which work

in general and others that are tied to specific algorithms.

For example, algorithms that implement regularization can

identify features that are left unpenalized and are thus more important.

While tree-based algorithms directly measure feature importance.

Other techniques can either inadvertently select different combinations of

features or drop features and continuously evaluate the performance of an algorithm.

To simplify this process,

these tasks are often implemented as part of a machine learning pipeline.

The third lesson focuses on principal component analysis,

which is a popular dimensional reduction technique.

This algorithm computes new features that are

combinations of the existing features in the original data set.

This process also computes the amount of variance in

the original data set that is explained by each new feature.

As a result, we can reduce the number of new features to meet

a predefined data reconstruction quality from our new set of features.

And in the process, generate a smaller set of

features with which to use in our machine learning algorithm.

The final lesson introduces a similar approach but

where we attempt to learn the distribution of

data in a potentially complex geometry

in order to find a much smaller set of descriptive features.

This type of algorithm is known as manifold learning,

which is an unsupervised technique to map

potentially nonlinear structures to many fewer dimensions.

Manifold learning is very popular as a visualization technique since

a high dimensional dataset can be mapped to two or three dimensions.

Thus allowing a complex dataset to be easily visualized.

Hopefully, you were intrigued by the potential of feature engineering.

This technique can not only improve your performance on a machine learning task,

but also aid in making the result understandable to a broader community,

both by reducing the number of dimensions included in the analysis,

as well as by enabling the visualization of

high dimensional data in a two or three dimensional plot. Good luck.

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