Technology

Feature Engineering and Machine Learning

What are the main idea and principles of Feature Engineering? Feature extraction (or engineering) is the process when engineers extract features from raw data to produce features that force machine learning processes. Well done feature extraction is a piece of art because it helps algorithms, making machine learning easier.

Stages that can help you to solve a problem in machine learning:

  • Collecting information
  • Filtration of information
  • Feature extraction
  • Determining the sample
  • Verification and forecasting of the result

Feature extraction is a crucial point in machine learning because, with its help, we can differentiate between bad samples and good samples. Let’s try to figure out what it means.

Imagine we have the following information: color, length, and width of the house. We have to predict the price of this house. But we all understand that price depends on the area of the house, not its width or length. We will create a new feature, “area of the house”. So now mache will learn much better because the new feature is connected to the price directly. Creating a new feature, “area of the house,” is the feature extraction.

Also, among ML software engineers are ubiquitous use of a crossed column and bucketed column. In the first case, we unite two columns with different information into one. So we created one feature. In the second case, we create different buckets for additional features, and only afterward do we add a new separate feature.

Sometimes, erasing the undesirable component is feature extraction as well.

Stages: 

  • Searching for the feature idea
  • Feature creation
  • Verification of compatibility with a sample
  • Repeat until it works flawlessly

This is how feature extraction works.

There are some very important principles to be successful in Feature Engineering:

  • Expertise that provides a clear understanding of the business goals and related measures of model performance;
  • Work through an iterative and systematic process;
  • Consideration of many possible options for Feature Engineering;
  • Understanding and controlling how feature selection affects model performance.

Feature engineering is the new alchemy of the modern world, where successful teams turn shared data into a value-added intellectual property for their organizations.

Few quotes about feature extraction

If we talk about feature extraction – this topic is almost not covered in media or modern books. But Feature extraction exploring is necessary for Machine Learning Development.

The better users try to understand feature engineering, the more successful machine learning will be.

Machine learning success is directly related to presenting the information. 

So, feature extraction is a procedure of changing basic information into features to improve the productivity of samples by making better predictions.

Feature extraction transforms raw data into understandable algorithms.

Indeed, feature extraction for Machine Learning is a topic of current interest. Of course, it needs a lot of research and information. Some progressive companies have started their development. Let’s keep watch over!

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