Tag: What is the Best Machine Learning Algorithms for Imbalanced Datasets

Posted on

What is the Best Machine Learning Algorithms for Imbalanced Datasets

Machine Learning Algorithms and Imbalanced Datasets

What is the Best Machine Learning Algorithms for Imbalanced Datasets?

In machine learning, imbalanced datasets are those where one class heavily outnumbers the others. This can be due to the nature of the problem or simply because more data is available for one class than the others. Either way, imbalanced datasets can pose a challenge for machine learning algorithms. In this blog post, we’ll take a look at which machine learning algorithms are best suited for imbalanced datasets and why they tend to perform better than others.

 For example, in a binary classification problem, if there are 100 observations, and only 10 of them are positive (the rest are negatives), then we say that the dataset is imbalanced. The ratio of positive to negative cases is 1:10. 

What is the Best Machine Learning Algorithms for Imbalanced Datasets
What is the Best Machine Learning Algorithms for Imbalanced Datasets

There are a few reasons why some machine learning algorithms tend to perform better on imbalanced datasets than others. First, certain algorithms are designed to handle imbalanced datasets. Second, some algorithms are more robust to outliers, which can be more common in imbalanced datasets. And third, some algorithms are better able to learn from a limited amount of data, which can be an issue when one class is heavily outnumbered by the others.

Some of the best machine learning algorithms for imbalanced datasets include:

– Support Vector Machines (SVMs),
– Decision Trees,
– Random Forests,
– Naive Bayes Classifiers,
– k-Nearest Neighbors (kNN),

Of these, SVMs tend to be the most popular choice as they are specifically designed to handle imbalanced datasets. SVMs work by finding a hyperplane that maximizes the margin between the two classes. This helps to reduce overfitting and improve generalization. Decision trees and random forests are also popular choices as they are less sensitive to outliers than other algorithms such as linear regression. Naive Bayes classifiers are another good choice as they are able to learn from a limited amount of data. kNN is also a good choice as it is not sensitive to outliers and is able to learn from a limited amount of data. However, it can be computationally intensive for large datasets.

There are two main types of machine learning algorithms: supervised and unsupervised. Supervised algorithms tend to perform better on imbalanced datasets than unsupervised algorithms. In this blog post, we will discuss why this is so and look at some examples.

Supervised Algorithms
Supervised algorithms are those where the target variable is known. In other words, we have training data where the correct answers are already given. The algorithm then learns from this data and is able to generalize to new data. Some examples of supervised algorithms are regression and classification.

Unsupervised Algorithms
Unsupervised algorithms are those where the target variable is not known. With unsupervised algorithms, we only have input data, without any corresponding output labels. The algorithm has to learn from the data itself without any guidance. Some examples of unsupervised algorithms are clustering and dimensionality reduction.

Why Supervised Algorithms Perform Better on Imbalanced Datasets
The reason why supervised algorithms perform better on imbalanced datasets is because they can learn from the training data which cases are more important. With unsupervised algorithms, all data points are treated equally, regardless of whether they are in the minority or majority class.

For example, in a binary classification problem with an imbalanced dataset, let’s say that we want to predict whether a customer will default on their loan payment or not. We have a training dataset of 1000 customers, out of which only 100 (10%) have defaulted on their loan in the past.

If we use a supervised algorithm like logistic regression, the algorithm will learn from the training data that defaulting on a loan is rare (since only 10% of cases in the training data are Positive). This means that it will be more likely to predict correctly that a new customer will not default on their loan (since this is the majority class in the training data).
However, if we use an unsupervised algorithm like k-means clustering, all data points will be treated equally since there is no target variable to guide the algorithm. This means that it might incorrectly cluster together customers who have defaulted on their loans with those who haven’t since there is no guidance provided by a target variable.

Conclusion:
In conclusion, supervised machine learning algorithms tend to perform better on imbalanced datasets than unsupervised machine learning algorithms because they can learn from the training data which cases are more important. 

Some machine learning algorithms tend to perform better on highly imbalanced datasets because they are designed to deal with imbalance or because they can learn from both classes simultaneously. If you are working with a highly imbalanced dataset, then you should consider using one of these algorithms.

Thanks for reading!

How are machine learning techniques being used to address unstructured data challenges?

Machine learning techniques are being used to address unstructured data challenges in a number of ways:

  1. Natural language processing (NLP): NLP algorithms can be used to extract meaningful information from unstructured text data, such as emails, documents, and social media posts. NLP algorithms can be trained to classify text data, identify key terms and concepts, and extract structured data from unstructured text.
  2. Image recognition: Machine learning algorithms can be used to analyze and classify images, enabling the automatic identification and classification of objects, people, and other elements in images. This can be useful for tasks such as image tagging and search, as well as for applications such as security and surveillance.
  3. Audio and speech recognition: Machine learning algorithms can be used to analyze and classify audio data, enabling the automatic transcription and translation of spoken language. This can be useful for tasks such as speech-to-text transcription, as well as for applications such as call center automation and language translation.
  4. Video analysis: Machine learning algorithms can be used to analyze and classify video data, enabling the automatic detection and classification of objects, people, and other elements in video. This can be useful for tasks such as video tagging and search, as well as for applications such as security and surveillance.

Overall, machine learning techniques are being used in a wide range of applications to extract meaningful information from unstructured data, and to enable the automatic classification and analysis of data in a variety of formats.

How is AI and machine learning impacting application development today?

Artificial intelligence (AI) and machine learning are having a significant impact on application development today in a number of ways:

  1. Enabling new capabilities: AI and machine learning algorithms can be used to enable applications to perform tasks that would be difficult or impossible for humans to do. For example, AI-powered applications can be used to analyze and classify large amounts of data, or to automate complex decision-making processes.
  2. Improving performance: AI and machine learning algorithms can be used to optimize the performance of applications, making them faster, more efficient, and more accurate. For example, machine learning algorithms can be used to improve the accuracy of predictive models, or to optimize the performance of search algorithms.
  3. Streamlining development: AI and machine learning algorithms can be used to automate various aspects of application development, such as testing, debugging, and deployment. This can help to streamline the development process and reduce the time and resources needed to build and maintain applications.
  4. Enhancing user experiences: AI and machine learning algorithms can be used to enhance the user experience of applications, by providing personalized recommendations, recommendations, or by enabling applications to anticipate and respond to the needs and preferences of users.

Overall, AI and machine learning are having a significant impact on application development today, and they are likely to continue to shape the way applications are built and used in the future.

How will advancements in artificial intelligence and machine learning shape the future of work and society?

Advancements in artificial intelligence (AI) and machine learning are likely to shape the future of work and society in a number of ways. Some potential impacts include:

  1. Automation: AI and machine learning algorithms can be used to automate tasks that are currently performed by humans, such as data entry, customer service, and manufacturing. This could lead to changes in the types of jobs that are available and the skills that are in demand, as well as to increased productivity and efficiency.
  2. Job displacement: While automation may create new job opportunities, it could also lead to job displacement, particularly for workers in industries that are more susceptible to automation. This could lead to social and economic challenges, including unemployment and income inequality.
  3. Increased efficiency: AI and machine learning algorithms can be used to optimize and streamline business processes, leading to increased efficiency and productivity. This could lead to economic growth and innovation, and could also help to reduce costs for businesses and consumers.
  4. Enhanced decision-making: AI and machine learning algorithms can be used to analyze large amounts of data and make more informed and accurate decisions. This could lead to improved outcomes in fields such as healthcare, finance, and education, and could also help to reduce bias and improve fairness.

Overall, the impact of AI and machine learning on the future of work and society is likely to be significant and complex, with both potential benefits and challenges. It will be important to consider and address these impacts as these technologies continue to advance and become more widely adopted.

  • [D] AISTATS 2025 Paper Acceptance Result
    by /u/zy415 (Machine Learning) on January 21, 2025 at 3:43 pm

    AISTATS 2025 paper acceptance results are supposed to be released today. Creating a discussion thread for this year's results. submitted by /u/zy415 [link] [comments]

  • [Research] Seeking Recommendations for an AI Model to Evaluate Photo Damage for Restoration Project
    by /u/ForeignMastodon4015 (Machine Learning) on January 21, 2025 at 2:37 pm

    Hi, everyone! I'm working on a photo restoration project using AI. The goal is to restore photos that were damaged during a natural disaster in my area. The common types of damage include degradation, fungi, mold, etc. I understand that this process involves multiple stages. For this first stage, I need an LLM (preferably) with an API that can accurately determine whether a photo is too severely damaged and requires professional editing (e.g., Photoshop) or if the damage is relatively simple and could be addressed by an AI-based restoration tool. Could you please recommend open-source, free (or affordable) models, preferably LLMs, that could perform this task and are accessible via an API for integration into my code? Thank you in advance for your suggestions! submitted by /u/ForeignMastodon4015 [link] [comments]

  • [D] Understanding predictive coding networks
    by /u/groundswell_ (Machine Learning) on January 21, 2025 at 12:06 pm

    Hi all, I'm trying to understand predictive coding networks like described in Rao & Ballard. So far I understand that training the network is done through setting the input (and output if training is supervised) and first modifying the activity of the neurons to reduce prediction errors, then modifying the synaptic weights. What I don't understand is that it seems the activity of a hidden layer "r" seems to be a function of the difference between the prediction and the input (see figure 1.b), it seems implied here that `r` is the product of the transposed weights UT and the prediction error which confuse me : I understand that we want to propagate the prediction error to the next layer, but how can we minimize (I - f(Ur)) if r = UT (I - f(Ur))? I think I still haven't fully grasped the overall architecture and would really appreciate if someone could help. submitted by /u/groundswell_ [link] [comments]

  • [D] Accumulation error
    by /u/Careless-Top-2411 (Machine Learning) on January 21, 2025 at 5:41 am

    Can anyone give me some work that has theorem/insight, about possible bounds or method to approximate error accumulation of sequential model? Something like the changes in distribution/error after each steps? submitted by /u/Careless-Top-2411 [link] [comments]

  • [Research] Who publish this gene expression dataset? 7070 genes, 69 samples, 5 classes: EPD, JPA, MED, MGL, RHB
    by /u/kidfromtheast (Machine Learning) on January 21, 2025 at 3:37 am

    Hi, my goal is to reference the original author and understand what is EPD, JPA, MED, MGL, RHB. The oldest reference I can found: 2008's paper [1], and the author's paper cite Dr. Gregory Piatetsky-Shapiro from KDnuggets and Prof. Gary Parker from Connecticut College. The most information I can get out of is it's a pediatric tumor dataset. 2009's paper [2], and the author's paper cite [3]. However, the paper mentioned only 42 patients samples. Meanwhile, the dataset I have 69 labeled samples and 23 unlabeled samples. Although I doubt it's the same paper, since paper [3] mentioned it's a 6,817 genes instead of 7,070 genes. But paper [2] add the complete name of each class based on paper [3]. So, I used archive website to check the dataset but it didn't archive the zip file. As of right now, I cannot check whether it is the same dataset. The last page I am visiting: https://web.archive.org/web/20060907191641/http://www.broad.mit.edu/mpr/CNS/ The link that I need: http://www.broad.mit.edu/mpr/CNS/#:~:text=Pomeroy_et_al_0G04850_11142001_datasets.zip [1]N. E. Ling and Y. A. Hasan, “Evaluation Method in Random Forest as Applied to Microarray Data,” Malaysian Journal of Mathematical Sciences, vol. 2, no. 2, pp. 73–81, 2008. [2]S. L. Pomeroy et al., “Prediction of central nervous system embryonal tumour outcome based on gene expression,” Nature, vol. 415, no. 6870, pp. 436–442, 2002, doi: 10.1038/415436a. [3]N. LING, “CLASSIFICATION OF MICROARRAY DATASETS USING RANDOM FOREST,” 2009. submitted by /u/kidfromtheast [link] [comments]

  • [D] Useful software development practices for ML?
    by /u/LetsTacoooo (Machine Learning) on January 21, 2025 at 3:00 am

    I am teaching a workshop on ML and I want to dedicate 2 hours to the software development part of building an ML system. My audience are technical undergraduate students that know python and command line. Any software practices (with links) you wish you knew when you were younger? Currently thinking of talking about git, code tests, validation (pydantic) and in terms of principles: YAGNI, KISS and DRY/WET code. Could also cover technical debt. submitted by /u/LetsTacoooo [link] [comments]

  • [D] ICLR 2025 paper decisions
    by /u/always_been_a_toy (Machine Learning) on January 20, 2025 at 7:50 pm

    Excited and anxious about the results! submitted by /u/always_been_a_toy [link] [comments]

  • [D] Uncertinity Quantificationfor time seriese prediction (RNN)?
    by /u/hunterh0 (Machine Learning) on January 20, 2025 at 6:57 pm

    I have a time series that predicts one of two classes at each step (0 or 1) using RNN, so it's sequence to sequence. I'm new to the topic of Uncertainty Quantification (UQ). Can I directly apply common methods such as deep-ensemble or MC dropout and simply expect everything to work? Are there any caveats? I have checked two libraries: torch-uncertinity and UQ-BOX but nothing is mentioned about time series. submitted by /u/hunterh0 [link] [comments]

  • [P] Anyone Experienced with Charting and Backtesting in Futures Trading?
    by /u/Embarrassed-Job-7847 (Machine Learning) on January 20, 2025 at 5:19 pm

    Hello everyone, I’ve been working on backtesting a theory related to trading futures around news events. The results so far have been promising, but I’d like to take things to the next level, potentially by incorporating machine learning or more advanced techniques. Does anyone here have experience with backtesting and integrating machine learning into trading strategies? Specifically for futures or similar instruments? I’d love to hear your insights, tips, or even resources that could help refine and expand this approach. Thanks in advance! submitted by /u/Embarrassed-Job-7847 [link] [comments]

  • [D] - Most Engaging ML Podcasts?
    by /u/DavesEmployee (Machine Learning) on January 20, 2025 at 4:06 pm

    Looking for good podcasts to stay on top of ML news. Specifically looking for ones that are able to tell a good story or narrative like Planet Money or Freakonomics rather than sounding like a lecture submitted by /u/DavesEmployee [link] [comments]

  • [R] Do generative video models learn physical principles from watching videos? Not yet
    by /u/Least_Light6037 (Machine Learning) on January 20, 2025 at 2:29 pm

    A new benchmark for physics understanding of generative video models that tests models such as Sora, VideoPoet, Lumiere, Pika, Runway. From the authors; "We find that across a range of current models (Sora, Runway, Pika, Lumiere, Stable Video Diffusion, and VideoPoet), physical understanding is severely limited, and unrelated to visual realism" paper: https://arxiv.org/abs/2501.09038 submitted by /u/Least_Light6037 [link] [comments]

  • Pre-trained models on faces/skin tones? [D]
    by /u/ThePresindente (Machine Learning) on January 20, 2025 at 12:56 pm

    I am doing a project that involves rPPG and I was woandering if there are any good pre-trained models on faces/skin tones that I can build on top. Thanks submitted by /u/ThePresindente [link] [comments]

  • [Discussion] How to Build a Knowledge Graph from Full Text Without Predefined Entities?
    by /u/OnlyBadKarma (Machine Learning) on January 20, 2025 at 9:36 am

    I'm building a knowledge graph from a large set of industry documents without predefined entities. How can I handle semantically duplicate entities and relationships effectively? Also, since I can't process all documents at once, how can I ensure consistency in extracted relationships when working in chunks? PS - Will be using GPT for processing submitted by /u/OnlyBadKarma [link] [comments]

  • [D] Llama3.2 model adds racial annotation
    by /u/randykarthi (Machine Learning) on January 20, 2025 at 9:35 am

    This is really interesting, I was conversing with Llama 3.2 3B model, I found out that it automatically appends or greets you based on your name. Maybe others already know this, but I just paid attention to this detail just now. Could it be because of the training dataset, or is this injected. check this out Edit: the racial annotation is not added to put a negative spin on this, it’s just an observation that salutations are customised based on the name. Which is an attention to detail submitted by /u/randykarthi [link] [comments]

  • [R] Evolving Deeper LLM Thinking
    by /u/hardmaru (Machine Learning) on January 20, 2025 at 4:23 am

    submitted by /u/hardmaru [link] [comments]

  • [R] Looking for retrieval datasets built from real documentation and queries
    by /u/KD_A (Machine Learning) on January 20, 2025 at 3:37 am

    Retrieval as in (query, passage) pairs where passage is a chunk of text from the documentation which is relevant to query. BeIR has good datasets, but the "documentation" is often pretty wide, e.g., any Wikipedia or PubMed article. I'm looking for a dataset where the documentation is more focused, something like scikit-learn's docs. StaRD is a high quality dataset, but it doesn't have enough queries for my purposes. Ideally, there are ≥5k unique queries. submitted by /u/KD_A [link] [comments]

  • [D] Looking for NLP annotation tool with custom column view
    by /u/V0dros (Machine Learning) on January 20, 2025 at 12:24 am

    Hi everyone! I'm working on a document revision project that requires NLP data annotation. I need a tool that can: Display the dataset in a standard tabular view Show git-style diffs between source and revised texts in a custom column I've already tried Argilla and Label Studio, but neither supports custom columns. Does anyone know of annotation tools that offer this functionality? Thanks in advance! submitted by /u/V0dros [link] [comments]

  • [D] The Case for Open Models
    by /u/Amgadoz (Machine Learning) on January 19, 2025 at 10:27 pm

    Why openness matters in AI submitted by /u/Amgadoz [link] [comments]

  • Any gift ideas for someone into ML? [D]
    by /u/Usernam3ChecksOuts (Machine Learning) on January 19, 2025 at 8:53 pm

    Hello everyone, I need help for a really special gift for someone who is really into Machine Learning and related fields and is doing research/a career in it. I know very little about Machine Learning, but I still want to get them something either really cool or practical for their work. Anything from buying them a new computer specifically for work or some cool collectible item. Anything including pointing me in a good direction would be appreciated, thank you! submitted by /u/Usernam3ChecksOuts [link] [comments]

  • [P] Noteworthy LLM Research Papers of 2024 (Part Two): July to December
    by /u/seraschka (Machine Learning) on January 19, 2025 at 3:46 pm

    submitted by /u/seraschka [link] [comments]

What is Google Workspace?
Google Workspace is a cloud-based productivity suite that helps teams communicate, collaborate and get things done from anywhere and on any device. It's simple to set up, use and manage, so your business can focus on what really matters.

Watch a video or find out more here.

Here are some highlights:
Business email for your domain
Look professional and communicate as you@yourcompany.com. Gmail's simple features help you build your brand while getting more done.

Access from any location or device
Check emails, share files, edit documents, hold video meetings and more, whether you're at work, at home or on the move. You can pick up where you left off from a computer, tablet or phone.

Enterprise-level management tools
Robust admin settings give you total command over users, devices, security and more.

Sign up using my link https://referworkspace.app.goo.gl/Q371 and get a 14-day trial, and message me to get an exclusive discount when you try Google Workspace for your business.

Google Workspace Business Standard Promotion code for the Americas 63F733CLLY7R7MM 63F7D7CPD9XXUVT 63FLKQHWV3AEEE6 63JGLWWK36CP7WM
Email me for more promo codes

Active Hydrating Toner, Anti-Aging Replenishing Advanced Face Moisturizer, with Vitamins A, C, E & Natural Botanicals to Promote Skin Balance & Collagen Production, 6.7 Fl Oz

Age Defying 0.3% Retinol Serum, Anti-Aging Dark Spot Remover for Face, Fine Lines & Wrinkle Pore Minimizer, with Vitamin E & Natural Botanicals

Firming Moisturizer, Advanced Hydrating Facial Replenishing Cream, with Hyaluronic Acid, Resveratrol & Natural Botanicals to Restore Skin's Strength, Radiance, and Resilience, 1.75 Oz

Skin Stem Cell Serum

Smartphone 101 - Pick a smartphone for me - android or iOS - Apple iPhone or Samsung Galaxy or Huawei or Xaomi or Google Pixel

Can AI Really Predict Lottery Results? We Asked an Expert.

Ace the 2023 AWS Solutions Architect Associate SAA-C03 Exam with Confidence Pass the 2023 AWS Certified Machine Learning Specialty MLS-C01 Exam with Flying Colors

List of Freely available programming books - What is the single most influential book every Programmers should read



#BlackOwned #BlackEntrepreneurs #BlackBuniness #AWSCertified #AWSCloudPractitioner #AWSCertification #AWSCLFC02 #CloudComputing #AWSStudyGuide #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AWSBasics #AWSCertified #AWSMachineLearning #AWSCertification #AWSSpecialty #MachineLearning #AWSStudyGuide #CloudComputing #DataScience #AWSCertified #AWSSolutionsArchitect #AWSArchitectAssociate #AWSCertification #AWSStudyGuide #CloudComputing #AWSArchitecture #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AzureFundamentals #AZ900 #MicrosoftAzure #ITCertification #CertificationPrep #StudyMaterials #TechLearning #MicrosoftCertified #AzureCertification #TechBooks

Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


Health Health, a science-based community to discuss human health

Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.

Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.

Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.

Turn your dream into reality with Google Workspace: It’s free for the first 14 days.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes:
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6 96DRHDRA9J7GTN6
63F733CLLY7R7MM
63F7D7CPD9XXUVT
63FLKQHWV3AEEE6
63JGLWWK36CP7WM
63KKR9EULQRR7VE
63KNY4N7VHCUA9R
63LDXXFYU6VXDG9
63MGNRCKXURAYWC
63NGNDVVXJP4N99
63P4G3ELRPADKQU
With Google Workspace, Get custom email @yourcompany, Work from anywhere; Easily scale up or down
Google gives you the tools you need to run your business like a pro. Set up custom email, share files securely online, video chat from any device, and more.
Google Workspace provides a platform, a common ground, for all our internal teams and operations to collaboratively support our primary business goal, which is to deliver quality information to our readers quickly.
 Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE
C37HCAQRVR7JTFK
C3AE76E7WATCTL9
C3C3RGUF9VW6LXE
C3D9LD4L736CALC
C3EQXV674DQ6PXP
C3G9M3JEHXM3XC7
C3GGR3H4TRHUD7L
C3LVUVC3LHKUEQK
C3PVGM4CHHPMWLE
C3QHQ763LWGTW4C
Even if you’re small, you want people to see you as a professional business. If you’re still growing, you need the building blocks to get you where you want to be. I’ve learned so much about business through Google Workspace—I can’t imagine working without it. (Email us for more codes)