One-Hot Encoding Feature Engineering Technique

One-Hot Encoding Feature Engineering Technique

Data is contained in different formats and may be recorded with columns that contain data that can be better adapted for data mining. There are situations in which you have to very few records of data, but those are important to consider. You also may have data that is missing in a column that you want to include in you data mining process so that you should assign a value or manage those missing values so that no error shows up during the calculations.

It is recognized that data can be better understood by splitting it and making it more readable for the machine learning algorithms so that Feature engineering is of upmost importance to overcome data storage and data structuring.

Not all data is well structured enough so that database and machine learning algorithms work like a well oiled machine, this is why feature engineering makes sense and applying the right techniques can greatly improve your data mining and data modeling procedures.

There are different feature engineering techniques that allow is to manipulate and structure data adequately. Some of the most remarkable and useful techniques known are:

  • Outliers
  • Log Transform
  • Imputation
  • One-Hot Encoding
  • Scaling
  • Feature Split
  • Grouping Operations
  • Binning
  • Handling
  • Extracting Date

One-Hot encoding is a popular technique because it simplifies several columns into one or more and reduce the data that is being analyzed without losing or affecting negatively the data mining process.

How One-Hot Encoding works? and What One-Hot Encoding Means?

This technique is widely used when we know about all possible features that are involved in our model and we want them to turn this data into a numerical value that can tell the machine learning algorithms that are present or absent at some point. The machine learning algorithms will take account on the record with a specific feature whether this has a value of 1 which means that the feature is present and that your model can consider all data related to this feature. On the other hand, your feature can be absent so that this case is represent with the value zero which indicates your machine learning algorithms no affectation to your model and calculations will be applied with out errors with this numerical specification.

Using zero and one values to turn categorical data into numerical one is what is known as One-Hot Encoding and it fostered data modeling performance.

IDEquipmentplasticStain SteelCopper
23D Printers001