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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.

  • [P] GPU Benchmark: Stable Diffusion v1.5 on 23 consumer GPUs (generating 460,000 fancy QR codes)
    by /u/SaladChefs (Machine Learning) on November 29, 2023 at 5:33 pm

    We benchmarked SD v1.5 on 23 consumer GPUs - To generate 460,000 fancy QR codes. The best performing GPU/backend combination delivered almost 20,000 images generated per dollar (512x512 resolution). You can read the full benchmark here: https://blog.salad.com/stable-diffusion-v1-5-benchmark/ Some key observations: Do not use the GTX series GPUs for production stable diffusion inference. Absolute performance and cost performance are dismal in the GTX series, and in many cases the benchmark could not be fully completed, with jobs repeatedly running out of CUDA memory. Additionally, many images generated on these GPUs came out all black, instead of as fancy QR codes as desired. There are very few surprises for which GPU is fastest for each backend. Newer GPUs with higher model numbers are faster in nearly all situations. Batching saves time and money. In most situations, you can expect anywhere from 5-30% savings using batch size 4, vs batch size 1. Generation time scales close to linearly with number of pixels. a 768x768px image has 2.25x the pixels as a 512x512px image, and typically takes around 2x the time to generate. You can get surprisingly good cost performance out of the 20-series and 30-series RTX GPUs, regardless of the backend you choose. If you have a use-case that allows you to take advantage of the optimizations offered by Stable Fast, and the engineering availability to build and maintain an in-house solution, this is a great option that could save you a bunch of money while providing a fast and reliable image generation experience for your users. Many factors go into scannability for these stable diffusion QR codes, and consistently getting good results is no simple task. Shorter URLs lead to better results, as there is less data to encode. Using QR codes with lighter backgrounds leads to easier scanning, but less interesting images. Some prompts work much better than others, and some prompts can sustain much higher guidance than others. In addition, iOS and Android phones use different QR scanning implementations, so some codes scan fine on one platform but not the other. Code and Docker Images stable-fast – https://github.com/chengzeyi/stable-fast Stable Fast QR Code Generator – https://github.com/SaladTechnologies/stable-fast-qr-demo Stable Fast QR Code Generator Docker Image: https://hub.docker.com/r/saladtechnologies/stable-fast-qr-code Automatic1111 – https://github.com/AUTOMATIC1111/stable-diffusion-webui Automatic1111 Dockerization: https://github.com/SaladTechnologies/stable-diffusion-webui-docker Automatic1111 Model Management: https://github.com/SaladTechnologies/a1111-dynamic Automatic1111 Docker Image: https://hub.docker.com/r/saladtechnologies/a1111 SD.Next – https://github.com/vladmandic/automatic SD.Next Dockerization: https://github.com/shawnrushefsky/automatic/ SD.Next Model Management: https://github.com/SaladTechnologies/sdnext-dynamic SD.Next Docker Image: https://hub.docker.com/r/saladtechnologies/sdnext ComfyUI – https://github.com/comfyanonymous/ComfyUI ComfyUI Dockerization: https://github.com/ai-dock/comfyui/ ComfyUI Model Management: https://github.com/SaladTechnologies/comfyui-dynamic ComfyUI Docker Image: https://hub.docker.com/r/saladtechnologies/comfyui Benchmark Worker Process: https://github.com/SaladTechnologies/qr-code-worker Queue Management Lambda: https://github.com/SaladTechnologies/benchmark-queues Database Access Lambda: https://github.com/SaladTechnologies/benchmark-api ​ https://preview.redd.it/62axtxgkob3c1.png?width=1900&format=png&auto=webp&s=a26b147b6a705260cb3e82b4c831fc99024d47d2 submitted by /u/SaladChefs [link] [comments]

  • [D] [P] Images to triangles
    by /u/Existing-Pie-3723 (Machine Learning) on November 29, 2023 at 4:56 pm

    Images to triangles Say you have an image, which is the input ... You now need to output a set of n triangles that will most closely replicate it, where n is fixed. How will you go about this problem today Genetic Algos have kind of tackled this problem, but takes forever or converges easily to some picture ... I tried running some preliminary things here on Neat playground here .... https://jerryjohnthomas.github.io/30pieces/ Each triangle is something takes space and color ... So try to group pixels of same color as triangle ... Like a knn thing I think we could maybe do a much better job without cnns, adding a simplifying assumption say the image indeed is made of n triangles and exact replication is possible. Is there anything else you would try. submitted by /u/Existing-Pie-3723 [link] [comments]

  • [discussion] text to voice generation for textbooks (non-math part)
    by /u/sweetchocolotepie (Machine Learning) on November 29, 2023 at 4:48 pm

    i was listening to lex podcast on some stuff i study and wanted to ask, are there any good natural-enough sounding local text to voice models out there? i would very much like to use it to turn the text parts of a book into an audio where i could listen to it while reading. i used edge's tts for speech by giving a paragraph to clipboard and to edge-tts in order to listen the text but it causes two problems: 1. you need internet connection and have the book opened 2. can only do paragraph by paragraph, and is prone to errors or sometimes if you use it too much it wont convert the full text afterwards. the idea would be to turn a chapter of a book into an audio file and transfer it so that i could listen to it on my mobile phone on the fly. what is the status of offline models where they could afford to output an okay voice (or even be able to give the voice of a tutor from their lectures and train it)? submitted by /u/sweetchocolotepie [link] [comments]

  • [D] Annotation Apps recommendation to draw ground truths for my project
    by /u/Certain-Seesaw-2274 (Machine Learning) on November 29, 2023 at 4:41 pm

    I am currently doing a project for my feature engineering class, the aim of the project is to detect field boundaries or roads. So for the ground truth I have used image segmentation from matlab which has various tools such as flood fill and others to fill the fields black and making the roads white. As you can see in the second image which is the ground that I have drawn, it has a lot of noise. Our professor suggested us to use iPad to draw groundtruth images. Can anyone please suggest apps for me drawn the same exact groundtruth but without noise submitted by /u/Certain-Seesaw-2274 [link] [comments]

  • [D] Model that takes a reference image and generates additional images of the subject?
    by /u/synapticpaint (Machine Learning) on November 29, 2023 at 4:13 pm

    Hi, I'm looking for a paper that I remember seeing and forgot to save. The paper discusses taking a single input image, e.g. with a person, and then generating additional images with the same person in other contexts. I vaguely remember a demo image of taking a painting of a woman as the reference image, then inpainting that woman into an existing image of an astronaut. I did some research and break-a-scene is the closest to what I'm talking about. But break-a-scene talks about extracting multiple tokens from the source image and I think the paper I'm looking for only talks about a single subject. And the demo image isn't there. Here are a bunch of papers that I found that do similar things that I also don't think are what I saw: https://realfill.github.io/ https://github.com/garibida/cross-image-attention https://github.com/SUDO-AI-3D/zero123plus https://omriavrahami.com/the-chosen-one/ https://omriavrahami.com/break-a-scene/ https://github.com/TencentARC/CustomNet https://damo-vilab.github.io/AnyDoor-Page/ https://github.com/OPPO-Mente-Lab/Subject-Diffusion Lmk if you know of any other papers that handles this or a related task, thanks! submitted by /u/synapticpaint [link] [comments]

  • [R] Survey of Consciousness Theory from Computational Perspective
    by /u/APaperADay (Machine Learning) on November 29, 2023 at 3:42 pm

    Paper: https://arxiv.org/abs/2309.10063 Abstract: Human consciousness has been a long-lasting mystery for centuries, while machine intelligence and consciousness is an arduous pursuit. Researchers have developed diverse theories for interpreting the consciousness phenomenon in human brains from different perspectives and levels. This paper surveys several main branches of consciousness theories originating from different subjects including information theory, quantum physics, cognitive psychology, physiology and computer science, with the aim of bridging these theories from a computational perspective. It also discusses the existing evaluation metrics of consciousness and possibility for current computational models to be conscious. Breaking the mystery of consciousness can be an essential step in building general artificial intelligence with computing machines. ​ submitted by /u/APaperADay [link] [comments]

  • [R] Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
    by /u/APaperADay (Machine Learning) on November 29, 2023 at 3:35 pm

    Paper: https://arxiv.org/abs/2311.08430 Abstract: Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale. https://preview.redd.it/n1l2fhle3b3c1.png?width=1360&format=png&auto=webp&s=772bbd4885bd87fb14461d76f8aca804b32fe1b8 submitted by /u/APaperADay [link] [comments]

  • [D] How to solve this document selection problem ?
    by /u/chat-alt-man-5540 (Machine Learning) on November 29, 2023 at 3:35 pm

    I'm currently working on a document selection problem and needed some inputs on how to proceed further in solving thisThe input I have is user data and I've to return a set of documents which match the user is talking about in the description. The list of documents is very huge around 2-3 Million records and they are very unstructured and user input necessarily might not be present in the document. Currently I've tried the following Create summary of all the documents using llm Create embedding of this and store it in vector db. Create summary of user input on the fly create embedding of the summarised user input and search in vector db and return top x documents with probability >=y This does get me documents very quickly but there are a lot of false positives and I'm not sure how to reduce these false positives. One of the thing I found is user query might not be present in the documents directly so in this case there are a lot of false positives. Is any any other way to solve this selection problem or reduce the number of false positives that come up in vector search ? I also tried re ranking with BM25 algorithm but it did not help a lot submitted by /u/chat-alt-man-5540 [link] [comments]

  • [D] what do these scores technically mean?
    by /u/Life_Ask2806 (Machine Learning) on November 29, 2023 at 2:36 pm

    when we benchmark different LLMs on different datasets (MMLU, TriviaQA, MATH, HellaSwag, etc.), what are the the signification of these scores? the accuracy? another metric? how can i know the metrics of each dataset (MMLU, etc.) https://preview.redd.it/ri4trwbwsa3c1.png?width=2158&format=png&auto=webp&s=44b2569de2a3e56e5e66ae340921a69c820f03b2 submitted by /u/Life_Ask2806 [link] [comments]

  • [R] Recommendations for ML/DL Workloads in Resource-Limited Environments
    by /u/E-fazz (Machine Learning) on November 29, 2023 at 2:21 pm

    Helloooo! I'm looking into the field of ML Systems, with a particular interest in executing deep learning tasks in environments with limited resources, such as commodity GPUs. Recently, I've noticed a significant focus on Large Language Models (LLMs) in contemporary research. However, I'm curious about other vital types of workloads in this domain. Could you please share your suggestions or insights on other meaningful ML/DL workloads that are crucial in resource-constrained settings? Your input would be greatly appreciated! submitted by /u/E-fazz [link] [comments]

  • Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks (and why fine-tuning is easily undone)
    by /u/hazardoussouth (Machine Learning) on November 29, 2023 at 2:20 pm

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

  • Recommendations for Interactive Online AI Training in Image Generation [Discussion]
    by /u/Ambitious_Mention455 (Machine Learning) on November 29, 2023 at 1:58 pm

    Hello everyone, I'm on the lookout for an online training program focused on AI in the domain of image generation for videos, images, and animations. Key requirements are: Interactive Format: A course that includes live classes with a teacher, not just pre-recorded video lectures. I'm looking for an interactive learning experience where I can engage directly with instructors. Language: The class must be conducted in English. Content Focus: I'm interested in AI techniques for generating images, videos, and animations and specifically interested in training that showcases and teaches using different platforms such as MidJourney, DALL-E, Adobe Firefly, Runway, Leonardo.ai, HeyGen, ClipDrop, RunDiffusion, etc I would greatly appreciate any suggestions for such courses or training programs. Personal experiences, links to courses, websites, or pointers to institutions offering this kind of training would be extremely helpful. Thank you in advance for your help! submitted by /u/Ambitious_Mention455 [link] [comments]

  • [P] GUIDE: Deploy YOLOv8 for live stream detection on Salad (GPUs from $0.032/hr)
    by /u/SaladChefs (Machine Learning) on November 29, 2023 at 1:55 pm

    Here's a step-by-step guide on how to deploy YOLOv8 on SaladCloud (GPUs start at $0.032/hr making YOLOv8 very affordable): https://docs.salad.com/docs/yolov8-step-by-step-deployment Deploying YOLOv8 on GPUs, we can process each video frame of a live stream in less then 10 milliseconds, which is 10 times faster then using a CPU. In this guide, we show: Reference architecture How to develop a fast API for real-time processing Setting up batch processing for asynchronous workloads Testing in a local environment Processing live video streams from Youtube Processing live video streams from RTSP, RTMP, TCP, IP address Implementing object tracking Running process on GPUs Storing object detection results in Azure storage ​ https://i.redd.it/ofuvtd5jla3c1.gif ​ submitted by /u/SaladChefs [link] [comments]

  • [D] About expert system
    by /u/Gullible-Leader3606 (Machine Learning) on November 29, 2023 at 1:45 pm

    I plan to learn expert system(CLIPS) and use it in my job, which is cybersecurity. Wonder if expert system are still in use in many areas. From my understanding expert system can actually sufficiently solve lots of real world problems. Suggestions are greatly welcomed! submitted by /u/Gullible-Leader3606 [link] [comments]

  • [R] NVAutoNet
    by /u/Neat_Cucumber_3282 (Machine Learning) on November 29, 2023 at 1:33 pm

    Did anyone read the new paper from NVIDIA called NVAutoNet and knows how did they get to the specified dimension of the feature maps? Because I tried to apply the operations from the table manually and don’t get the same result as the formula. Do they use any kind of padding or if anyone can explain how the operations are applied. Thank you. submitted by /u/Neat_Cucumber_3282 [link] [comments]

  • [R] Generating Multidimensional Clusters With Support Lines
    by /u/FakenMC (Machine Learning) on November 29, 2023 at 11:47 am

    https://arxiv.org/abs/2301.10327 Clugen is a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions. Clugen is open source, comprehensively unit tested and documented, and is available for the Python, R, Julia, and MATLAB/Octave ecosystems. ​ Various examples in 3D. submitted by /u/FakenMC [link] [comments]

  • [R] "It's not just memorizing the training data" they said: Scalable Extraction of Training Data from (Production) Language Models
    by /u/wojcech (Machine Learning) on November 29, 2023 at 10:45 am

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

  • [D] Student training objective in "knowledge distillation" or "teacher-student method"
    by /u/txhwind (Machine Learning) on November 29, 2023 at 9:46 am

    I have seen 3 kinds of objectives. Which is most popular? Which is most useful? match some hidden states of teacher It seems from the original knowledge distillation paper, but I never used it in practice because of the complexity and model structure limitation match the output logits of teacher (maybe with temparature) match the predicted labels of teacher I like this one because: 1. not limited by model structure at all; 2. no extra objective code in student training; 3. I can save the teacher output as a normal dataset for further exploring submitted by /u/txhwind [link] [comments]

  • MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers [R]
    by /u/we_are_mammals (Machine Learning) on November 29, 2023 at 8:36 am

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

  • [D] Is it legal to train on audio that is copyright infringing but not traditionally copyrighted?
    by /u/SuperwhizAJ (Machine Learning) on November 29, 2023 at 8:29 am

    I want to train a model for a musician based on piano covers they have recorded of popular songs and therefore own, but because of copyright infringement they still pay royalties to the writers of these songs. Curious if this is legal or not -- I would presume not since the recordings are not being monetized in this scenario but rather fed into a model, and the recordings are still owned 100% by the musician licensing them to me. submitted by /u/SuperwhizAJ [link] [comments]

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