Do it for meDo it for me

  • Home
  • Business Services
    • Finance
    • Legal
  • IT Services
    • Artificial Intelligence
    • Graphic Design
    • Marketing
    • Mobile Apps
    • Stories
    • Web Design
  • Trainings
    • Business Training
    • IT Training
  • Contact
  • No products in cart.
SHOPNOW

Dimensionality Reduction in ML

Do It For Me
Friday, 22 October 2021 / Published in Do IT For Me

Dimensionality Reduction in ML

“Machine Learning is the future of humans and the universe.”

At the time of final classification, there are too many factors present. These factors are variables. If we increase the number of features it becomes difficult to visualize the training data. Moreover, it makes working on the training set harder. Sometimes the features are correlated and hence they are redundant. To solve this Dimensionality Reduction comes into the picture. It is a technique that results in a lesser number of input variables in the dataset. High-dimensionality measurements and dimensionality reduction procedures are frequently utilized for information representation. In any case, these strategies can be utilized in applied ML to improve the classification or regression accuracy of the dataset. As a result, it makes a better fit for the predictive model.

6 Essential Advantages of Pandas Library

Source

Components of Dimensionality Reduction

There are two components of Dimensionality reduction:

Feature Selection

It is a process of selecting a subset of features that seems more relevant while using the model. It simplifies the models which will be easy to interpret by users. Moreover, it avoids the curse of dimensionality. Mostly, it involves three ways i.e., Embedded, Filter & Wrapper.

Feature Extraction

It reduces the set of raw data into manageable groups for processing. One of the most important characteristics of these datasets is that they require a lot of computing resources to process. It reduces the amount of data but it still completely and accurately describes the original dataset. 

Why is Dimensionality reduction important?

The issue of undesirable expansions in measurement is firmly identified with others. That was to the obsession with estimating/recording information at a far granular level than it was done in the past. This is not the slightest bit recommending that this is a new issue. It has begun acquiring significance recently because of a flood in information. The sensors used in businesses are widely expanding. These sensors constantly record information and store it for investigation at a later point. As a result of so much data, there can be redundancy.

Source

Source

Methods to perform Dimensionality Reduction

  • Missing Value

While investigating the dataset, on the off chance that we experience missing qualities, what do we do? Our initial step ought to be to distinguish the explanation. Then, at that point, need to attribute missing qualities/drop factors utilizing suitable strategies. Be that as it may, imagine a scenario in which we have too many missing qualities. Would it be advisable for us to attribute missing qualities or drop the factors? At that time we should use mean, mode, and median values and we replace them with missing values.

  • Low Variance

How about we think about a situation where we have a steady factor (all perceptions have a similar worth, 5) in our dataset. Do you figure it can work on the force of the model? NOT, because it has zero difference.

  • Decision Trees 

We can utilize it as an extreme answer for tackling different difficulties. Like missing values, outliers, and recognizing significant variables. Many data scientists utilized decision trees and it functioned admirably for them.  Indecision trees, internal nodes represent the features of the dataset. As well as branches represent the decision rules while each leaf node represents the outcome. 

  • Random Forest 

Random Forest is an ensemble of decision trees. Moreover, it develops a group of decision trees to classify data objects.  One of the main parameters of random forest is n_estimator because it decides the number of decision trees. It can perform both classification and regression. The higher the number of trees in the forest, the higher is accuracy. The higher number of trees in the random forest also prevents the problem of overfitting. Random forest is more biased for the variables that have more no. of distinct values. As a result, it favors numerical variables more than binary or categorical values. 

  • High Correlation 

Dimensions showing higher correlation can drop down the presentation of a model. Besides, it isn’t a great idea to have various factors of comparable data. You can utilize the Pearson connection framework to recognize the factors with high correlation. Furthermore, select one of them utilizing VIF (Variance Inflation Factor). Factors having a higher worth ( VIF > 5 ) can be dropped.

  • Principal Component Analysis (PCA)

Karl Pearson has presented this technique. Likewise, it chips away at a condition. That says while the information in a higher-dimensional space needs to map the information in lower measurement space. Albeit, the variance of the data in the lower dimensional space ought to be most extreme. 

It includes two steps: 

  • Make the covariance matrix of the dataset. 
  • Find the eigenvectors of this matrix. 

We use Eigenvectors relating to the biggest eigenvalues. That is to recreate a huge part of the variance of the original dataset. Henceforth, the number of eigenvectors is less. Also, there may have been some data loss all the while. However, the remaining vectors hold the main variance.

What did we learn?

To sum up this article we have studied dimensionality reduction in a lot of detail. Now, we know why dimensionality reduction in machine learning is an important part of preprocessing the data. Dimensionality is the number of variables, features, or columns which are present in the given dataset. The process of reducing these features is dimensionality reduction. The most professional definition of dimensionality reduction will be stated as: “It is a way of converting the higher dimensions dataset into lesser dimensions dataset ensuring that it provides similar information.”

 

Sources

For more articles, CLICK HERE.

Related

  • Tweet
Tagged under: Components, Decision Trees, Dimensionality Reduction, Feature Extraction, Feature Selection, High Correlation, Low Variance, Principal Component Analysis (PCA), Random Forest

What you can read next

How to Shift Your Established Business Online?
5 Reasons why you cannot ignore social media while building business
Neural Network in Deep Learning

1 Comment to “ Dimensionality Reduction in ML”

  1. DNA based Data Storage - DoItForMe Tech Community says :Reply
    November 18, 2021 at 7:23 am

    […] For more articles, CLICK HERE.  […]

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recent Posts

  • Quantity vs quality in blogging
  • Biggest AI trends for 2022 and the years Ahead
  • Paid marketing vs organic marketing. What’s best for you?
  • Should You Be Spending More Time And Money On Your Website?
  • Top 10 things your business marketing needs in 2022

Recent Comments

  1. Evolution of E-Commerce in the Last Decade - Do it for me on Digital Marketing – Oxygen for Online Business
  2. Purpose and Types of Genetic Engineering - Do it for me on How AI Is Changing The World?
  3. Use of Technology in Military  - Do it for me on Impact of Technology on Human Creativity
  4. You, use them, love them, but Do You know them? - Emojis - Do it for me on How To Combat The Emerging Problem Of Social Media Addiction?
  5. Everything You Need to Know About YouTube Marketing - Do it for me on SEO Guide For Beginners

Recent Posts

  • Quantity vs quality in blogging

    There has long been controversy regarding the a...
  • Biggest AI trends for 2022 and the years Ahead

    In 2022 we will see artificial intelligence tak...
  • Paid marketing vs organic marketing. What’s best for you?

    If you don’t understand this one simple thing a...
  • Spending-time-and-money-on-website

    Should You Be Spending More Time And Money On Your Website?

    Why do you need a website? What is the need for...
  • Top 10 things your business marketing needs in 2022

    The year’s end is an extraordinary opport...

Recent Comments

  • Evolution of E-Commerce in the Last Decade - Do it for me on Digital Marketing – Oxygen for Online Business
  • Purpose and Types of Genetic Engineering - Do it for me on How AI Is Changing The World?
  • Use of Technology in Military  - Do it for me on Impact of Technology on Human Creativity
  • You, use them, love them, but Do You know them? - Emojis - Do it for me on How To Combat The Emerging Problem Of Social Media Addiction?
  • Everything You Need to Know About YouTube Marketing - Do it for me on SEO Guide For Beginners

Archives

  • November 2022
  • September 2022
  • August 2022
  • March 2022
  • December 2021
  • October 2021
  • September 2021
  • August 2021
  • July 2021
  • June 2021
  • May 2021
  • April 2021
  • March 2021
  • January 2021
  • December 2020
  • November 2020
  • October 2020
  • September 2020

Categories

  • Artificial Intelligence
  • Do IT For Me
  • Mobile apps
  • Online Marketing
  • Social Media
  • Web Development

Meta

  • Log in
  • Entries feed
  • Comments feed
  • WordPress.org

Latest news straight to your inbox.

IT SERVICES

  • Artificial Intelligence
  • Marketing
  • Mobile Apps
  • Web Design

BUSINESS SERVICES

  • Business Growth Plan
  • Finance
  • Legal
  • Pro bono

QUICK LINKS

  • About
  • Careers
  • Blog
  • Contact

CONTACT US

Email

info@difm.tech 

Phone

678-888-TECH 

©2017-2022. Do It For Me DIFM.Tech. All rights reserved.

TOP