Overview dari Feature Extraction

Data that has been collected and obtained from various types of sources often has a level of difficulty that is quite difficult and even very complex in its management to be analyzed according to its needs and sometimes also contains many types of information that are unrelated or irrelevant. In some cases, the data coming from the dataset can become too large or messy, which hinders the process of analyzing and even modeling the data efficiently. Therefore, a data processing stage is needed that aims to retrieve the most relevant information and ensure good data quality and in accordance with the set objectives.

In some cases using datasets, it is known that there are hundreds or even thousands of features (columns) that may not all be related (relevant) for analysis and modeling (data visualization) in accordance with the objectives set. If we use all these features in creating a model (data visualization), it can cause various types of problems such as overfitting and worsening model performance. Feature extraction can help reduce the type of data dimension by selecting the most important and relevant features.

Introduction To Feature Extraction

What is Feature Extraction? Feature Extraction is the process of taking data or related information (relevant) from raw data or datasets that are generally complex to be used in conducting analysis or doing data modeling (data visualization). To use Feature Extraction can be done through the Pandas Library using the Python programming language, Feature Extraction is a technique used to extract various types of important features that suit your needs and come from DataFrame or Series so that the data can be used to perform further analysis (advanced) and in making models (data visualization).

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from skimage.io import imread, imshow
image = imread("/content/pexels-photo-1108099.jpeg")
imshow(image)

Example of writing Feature Extraction on the Pandas Library using the Python programming language. Source: Great Learning

Benefits of Using Feature Extraction in Pandas Library

Here are some of the benefits of using Feature Extraction in the Pandas Library (especially for a Data Scientist and Data Analyst):

  1. Support for improving Model Performance: By using various types of features that are not relevant and informative can certainly improve model performance from machine learning. Feature Extraction helps a Data Scientist and Data Analyst to select the most influential features and in accordance with the desired model results.
  2. Reduce the chances of Overfitting: By removing or reducing various types of redundant features, feature extraction can help data scientists and data analysts avoid overfitting. A well-customizable model is more likely to work better on the new data provided.
  3. Reduce Mismatched Data Dimensions: Some datasets have thousands or more features, which can affect the performance and efficiency of calculations performed. Feature extraction can help a data scientist and data analyst to reduce the dimension of data by selecting only the most important types of features.
  4. Better Understanding of Data: Using various types of relevant features can help someone who works in the data field (such as Data Scientists and Data Analysts) to understand data better. The different types of features resulting from extraction are often easier for the resulting interpretation to do.
  5. Prevent Inappropriate Data: Often found datasets or raw data that have a format that is not suitable for analysis and modeling. Feature extraction can help a Data Scientist and Data Analyst to overcome this problem by converting or converting data derived from datasets to a more suitable format.
  6. Reduce Computation Time: By using only the essential features, a data scientist and data analyst can reduce the computational time required to run and train a model, especially if the dataset provided is very large.
  7. Prevent Multiple Data Types: Data often has a mix of data types, such as strings, integers, and other categorical data. Feature extraction can help a data scientist and data analyst to overcome this problem by converting or converting data to the appropriate data type for analysis and modeling.
  8. Solve Problems at the Data level: With a very high level of data, such as in the use of Computer Visual (CV) or Neuro Linguistic Programming (NLP), Feature Extraction can help a Data Scientist and Data Analyst to produce various types of features that are more abstract and can describe data better.