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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

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:

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

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.
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.
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.
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:

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.
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.
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.
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:

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.
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.
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.
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] Training/finetuning a LLM
by /u/11igor (Machine Learning) on September 25, 2023 at 1:12 pm
Hey! Months ago, I was fascinated by Karpathy’s nanoGPT project - the ability to train a small LLM on your text file seemed very interesting to me. I tried training it on my chat history to build some inifinite chat-generator for fun, but unfortunately, the results were bad. Recently I had even worse experiences with newly-released ChatGPT 3.5 fine-tuning. Are there any good, simple ways to train/fine-tune LLMs now? I would love something that could train on an Apple M2 processor (like Karpathy’s nanoGPT), or Colab, or cheap API (like ChatGPT fine-tuning). submitted by /u/11igor [link] [comments]
[R] Microsoft Researchers Announce CodePlan: Automating Complex Repo-Level Software Engineering Tasks with AI
by /u/Successful-Western27 (Machine Learning) on September 25, 2023 at 12:27 pm
As software projects grow, changing code across entire repositories becomes tedious & error-prone. Tasks like migrating APIs or updating dependencies require complex edits across files. I explored a new approach from Microsoft Research to automate these "repository-level" coding challenges with AI. Their new paper proposes CodePlan - an AI system that breaks repository tasks into incremental steps guided by planning & analysis. Key points: Uses LLMs like GPT-3 for localized code edits Maintains validity across repository via incremental analysis Adaptively plans multi-step changes based on code dependencies Significantly outperformed baselines on API migration & temporal edits Automated tasks across 168 file C# codebase 2-3x more accurate edit locations than baselines Produced final valid codebases, unlike reactive approaches The core insight is combining LLM strengths with rigorous planning based on dependency analysis. This automates interdependent code changes that naive LLM use struggles with (I personally have these kinds of issues all the time with GPT4 - lack of context about the entirety of the repo/how files fit together). I think CodePlan demonstrates AI can expand beyond small coding assists into large-scale engineering tasks. Planning + LLMs > LLMs alone. This could really improve productivity and code quality... at least for me 🙂 Full summary. Arxiv paper: https://arxiv.org/pdf/2309.12499.pdf submitted by /u/Successful-Western27 [link] [comments]
[D] Distillation understanding
by /u/Grumlyly (Machine Learning) on September 25, 2023 at 11:32 am
In the main scenario, the smaller model learns from the same data as the bigger model and also from the predictions of the bigger model and incorporate the 2 output labels on a specific loss. Basically, it is equivalent to say to the smaller model : "be careful this example is hard" in the case that big model divergence from true output? I am missing something? submitted by /u/Grumlyly [link] [comments]
Baby Sleep Tracker using a basic SVM [P]
by /u/GoochCommander (Machine Learning) on September 25, 2023 at 11:16 am
I made a FOSS baby sleep tracking system. The system tracks wake/sleep status, and informs the user when their baby is likely to need a nap next. But it stopped working as soon as my baby started sleeping on his stomach, and started using blankets. The original version relied on anatomical features being visible. This version delivers the ability to train a blank slate SVM binary classifier on pictures of a user's baby, making it extremely biased (and resilient) to the custom behaviors the user's baby exhibits (blanket covering baby, teddy bear/other objects in crib, etc.). All generated data stays on your machine, nothing leaves the LAN. Video: https://youtu.be/8i0wHA_knKc?si=uhA4PpOYP0jMKLz1 For obvious reasons I didn't have a dataset of babies sleeping.. so I wrapped a python/flask service with a React app which lets a user press a button to train the model w/ a new image from the camera's live stream. Then this model is invoked over time (+ other heuristics) to determine whether your baby is present and sleeping. I believe it works better than $300+ systems sold on the market, open sourced it: https://github.com/calebolson123/BabySleepCoach I'm thinking a fun next step for this project could be to apply privateGPT on the feature-engineered sleep records for a true "Sleep Coach" submitted by /u/GoochCommander [link] [comments]
[D] Does granger causality work for time series with different frequencies
by /u/Pineapple_throw_105 (Machine Learning) on September 25, 2023 at 11:04 am
Is there a Granger test where series are a quarterly one and a weekly one? submitted by /u/Pineapple_throw_105 [link] [comments]
[R] Seeking Insights on AI Security Challenges: Short Survey
by /u/Agile_Temperature678 (Machine Learning) on September 25, 2023 at 8:43 am
Hello everyone, I'm conducting a research survey on the challenges and gaps in AI security. Given the expertise in this community, I believe your feedback would be invaluable in shaping the future of AI security solutions. The survey takes less than 10 minutes and delves into current practices, perceptions, and needs related to AI security. If you have experience or insights in this area, I would greatly appreciate your participation. Survey Link: https://forms.gle/i9AefyL8izyt9QjX6 All responses will remain anonymous, and the collected data will only be used for research purposes. Additionally, if you're open to a deeper discussion on this topic, there's an option within the survey to indicate your interest. Thank you in advance for your time and insights! If you have any questions or additional thoughts, please don't hesitate to comment below. submitted by /u/Agile_Temperature678 [link] [comments]
[P] Looking for a pretrained Embedding model that would work well with Network Logs
by /u/Bulky-Programmer-291 (Machine Learning) on September 25, 2023 at 7:51 am
Hi, I'm working on RAG on Network Logs. Having trouble with the retrieval part as the embedding model I'm currently using doesn't retrieve logs based on user prompt similarity. submitted by /u/Bulky-Programmer-291 [link] [comments]
[D] How do you send data in batches to an open source LLM to be processed on GPU
by /u/redd-dev (Machine Learning) on September 25, 2023 at 6:46 am
Say for eg. I am doing sentiment analysis using Llama 2. I have daily news articles which I wish to get daily sentiment ratings. Rather than looping daily in my Python script or prompt template, how do I send say 30 days of daily news in a batch to Llama 2 to get back 30 daily sentiment ratings in one go so that I am fully utilizing my GPU resources? submitted by /u/redd-dev [link] [comments]
[P] Psychoanalysing ChatGPT Using Statistics To Make a Decent Dating App
by /u/fuckinghelldad (Machine Learning) on September 25, 2023 at 5:56 am
submitted by /u/fuckinghelldad [link] [comments]
[D] How does DDIM work?
by /u/furrypony2718 (Machine Learning) on September 25, 2023 at 5:45 am
The Wikipedia page on Diffusion Models has been pretty minimal for an entire year. I feel like it should be fixed, so I fixed it finally. It strikes me odd that such a hot topic has such atrociously bad Wikipedia. I feel duty-bound to educate the near-future AI, since they'll be reading Wikipedia for the next few years at least. Currently I think it's mostly complete, but I still don't understand the mathematical details of DDIM (I tried reading the paper and could not understand it), or generally how it is possible to sample without noise. This is a serious problem since as far as I see most of practical diffusion models use deterministic sampling, and they are all based on the same principle as DDIM. If anyone could explain simply what DDIM is really doing that would be great. I understand part of the paper: that they constructed an entire family of distributions over trajectories that has the same two-point marginals. I also haven't got much in the section on Examples. If you think there are some interesting examples of Diffusion Models, please comment below. submitted by /u/furrypony2718 [link] [comments]
[Discussion] train supervised model to generate custom embeddings
by /u/greenspywork (Machine Learning) on September 25, 2023 at 4:25 am
Hello, Can someone provide pointers on how to create a model who's job is to create highly relevant vector embeddings from supervised data? For example, if I have a bunch of sentences with labels (cats, dogs, etc.) describing the sentences, the model should generate embeddings given new sentences about cats which are very similar. I know a general text embedding model can do this, however can labelled data be used to make the embeddings better? What process, type of model, or whatever can be done to accomplish this? Two tower models seem close to what I am looking for, but not quite. Any suggestions? Thanks. submitted by /u/greenspywork [link] [comments]
[D] How has work changed for you given the shift from growth to profitability?
by /u/Terrible-Hamster-342 (Machine Learning) on September 25, 2023 at 4:24 am
For the data scientists/applied scientists/research scientists - What kind of projects are you working on now that the economy has shifted and companies are focusing more on profitability than on growth? What techniques have worked for you and what are you looking into as potential solutions? An example would be - optimizing your marketing campaign spend in channels that give you the most bang for your buck vs just spending arbitrarily to acquire new users. submitted by /u/Terrible-Hamster-342 [link] [comments]
[R] LEAP Hand: Low-Cost (<2KUSD), Anthropomorphic, Multi-fingered Hand -- Easy to Build (link in comments)
by /u/pathak22 (Machine Learning) on September 25, 2023 at 2:33 am
submitted by /u/pathak22 [link] [comments]
Help with Qn 4 [Discussion]
by /u/Proof-Mongoose3850 (Machine Learning) on September 25, 2023 at 2:06 am
submitted by /u/Proof-Mongoose3850 [link] [comments]
[P] OpenGL-based inference engine
by /u/mtnwrw (Machine Learning) on September 25, 2023 at 12:15 am
I created an OpenGL/OpenGLES based inference framework a while back which is rather GPU-agnostic and might be a good option for distributing multi-platform ML solutions for platforms ranging from Android over desktop to WebGL(2). Quite recently I added support for LLMs to that (restricted to 4-bit quantized Llama models for now). The LLM-enabled fork can be found here (compileable sample code inside). Maybe someone finds this useful. Also looking for collaborators to extend the functionality. submitted by /u/mtnwrw [link] [comments]
[P] I create a small pytorch utility to Import custom dataset
by /u/charles_data_dev (Machine Learning) on September 24, 2023 at 11:46 pm
Hi guys , TorchClassifierData is A small pytorch utility to Import, Split ,Normalize and Visualize custom dataset for classification tasks. wich is indispensable for real word problem . You can find a full notebook that use TorchClassifierData to train a classifier on this kaggle dataset here. The code source is avalaible on my github. Thank you. submitted by /u/charles_data_dev [link] [comments]
[D] Why do Diffusion models work so well while SG-MCMC does not?
by /u/Dangerous-Flan-6581 (Machine Learning) on September 24, 2023 at 11:03 pm
Diffusion models are basically Langevin sampling. What are the key differences and tricks that set them apart from Langevin dynamics? Why do they work so well while very similar sampling methods don't? submitted by /u/Dangerous-Flan-6581 [link] [comments]
[P] Hardware Resources for training SwinBert
by /u/Big-Brain_69 (Machine Learning) on September 24, 2023 at 9:56 pm
So I've been thinking of implementing SwinBert for a college project and have been wondering what all resources i would be needing for a computer. Any ideas? submitted by /u/Big-Brain_69 [link] [comments]
[R] Researchers announce GPT4Tools: a method for teaching LLMs how to use tools for visual tasks
by /u/Successful-Western27 (Machine Learning) on September 24, 2023 at 7:11 pm
LLMs are great with words but can't handle visual tasks like understanding images. Teaching them to use visual tools could make them much more capable. A new paper introduces GPT4Tools - a method to efficiently teach existing LLMs to invoke tools for visual tasks without proprietary data. My highlights from the paper: Uses ChatGPT as a "teacher" to generate instructional data for other LLMs Fine-tunes LLMs like Vicuna on this data using selective weight tuning (keeps base model frozen) Allows smaller 13B LLM to match 175B GPT-3.5 on seen tools after tuning Data augmentation with negative/context samples was found to be the secret sauce to get this to work Can generalize to brand new visual tools in a zero-shot way This is big because it shows we may not need hyper-expensive training of massive models to impart visual capabilities to LLMs. They seems to be generalizable enough that they can be taught to work with images. Some examples shown include counting objects or segmenting items in pictures using other tools. With this approach, existing models can be made multi-modal! Pretty cool. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]
[D] Tools to gather and collaborate on fine-tuning datasets?
by /u/zeJaeger (Machine Learning) on September 24, 2023 at 6:42 pm
Hey all, I run a small team & we are collaborating on a few data sets that we use to fine-tune GPT3.5, We are currently using Google Sheets and I'm wondering if there is a tool where we can organize our data preferably with version control Any ideas? submitted by /u/zeJaeger [link] [comments]