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.
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 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 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.
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:
- 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] Duplicating layers in large modelsby /u/floriv1999 (Machine Learning) on October 1, 2023 at 11:34 am
Is there any notable work on duplicating layers in large feed forward models? In contrast to e.g. the brain which is essentially a directed graph most networks utilized nowerdays use a feed forward approach. E.g. transformers are able to attend to past tokens, but generate the tokens in a way where for a given token a given weight is not utilized at different stages in the feed forward pass. In my intuition this would lead to an issue where concepts (factual data as well as learned "algorithms") might be duplicated as they are needed at different depths in the generation process and are sequentially dependent on one another. This does not directly make the model less capable, as it might learn the same concept at two layers sufficiently well, but it reduces the data and parameter efficiency and and might impact generalization capabilities. Using a full on brain like graph might be hard to implement/optimize/scale on current hardware and is tricky with the backprop. But is there any work on duplicating a few layers, placing them at different depths in large models. I would guess that this would be more impactful for large models. One would essentially trade compute for better data efficiency. submitted by /u/floriv1999 [link] [comments]
- [n] Introducing r/AudioAI: Any AI You Can Hear!by /u/chibop1 (Machine Learning) on October 1, 2023 at 12:52 am
I couldn't find any AI sub dedicated to audio, so I’ve created r/AudioAI to serve as a hub for everything at the intersection of artificial intelligence and the world of sounds. AI-driven music, speech, audio production, and all other AI audio technologies. If anyone wants to be part of mod, let me know! submitted by /u/chibop1 [link] [comments]
- [P]Handling categorical missing data in churn prediction model for telecom databy /u/guyloveskissing (Machine Learning) on September 30, 2023 at 10:22 pm
I am working on a telecom dataset where I need to fit a model to for predicting churn(yes or no). There are a lot of categorical data with missing values( total values 7043). What is the best way to handle missing data in this case, is it better to ignore it or any other better imputation method? Data columns (total 21 columns): customerID 7043 non-null object gender 7043 non-null object Age 7043 non-null int64 Partner 7043 non-null object Dependents 7043 non-null object tenure 7043 non-null int64 PhoneService 7043 non-null object MultipleLines 6500 non-null object InternetService 6500 non-null object OnlineSecurity 7043 non-null object OnlineBackup 7043 non-null object DeviceProtection 7043 non-null object TechSupport 7043 non-null object StreamingTV 6500 non-null object StreamingMovies 6500 non-null object Contract 6500 non-null object PaperlessBilling 7043 non-null object PaymentMethod 6500 non-null object MonthlyCharges 7043 non-null float64 TotalCharges 7043 non-null object Churn 7043 non-null object submitted by /u/guyloveskissing [link] [comments]
- [D] (How) Can you estimate inference speed of a NN model on given hardware?by /u/teleoflexuous (Machine Learning) on September 30, 2023 at 7:10 pm
How, outside of testing, do you estimate how quickly a specific model will run on some hardware? Anything about time is rarely mentioned in papers and if it is, it's more likely to talk about training, unless authors are specifically proud of their speed (like YOLO). Even less so in any README. Some way to translate numbers of parameters into seconds on a given GPU/CPU, any rules of thumb better than just setting up everything every time? submitted by /u/teleoflexuous [link] [comments]
- [D] How do I begin with AI ?by /u/Dry_Ad_3887 (Machine Learning) on September 30, 2023 at 5:15 pm
I'm fairly new to the Al domain. I've decent python knowledge. I've gone through a lot of YouTube tutorials and got stuck in the tutorial hell. After struggling through hours of videos came here as my only last hope !!. How do I begin? What python frameworks should I learn? Which particular books should I refer ? submitted by /u/Dry_Ad_3887 [link] [comments]
- [D] Struggling to get interviews what to do?by /u/AbjectDrink3276 (Machine Learning) on September 30, 2023 at 4:51 pm
Edit: I am a USA citizen so no need for sponsorship. I have 4 yoe in a start up company and a phd four publications 2 in high level math journals and 2 CV/DL papers in A journals and also 4 patents. I have experience with most common Cv tasks eg object detection, Multi object tracking, 2d/3d human pose estimation and monocular depth estimation. I’m well versed in typical network building blocks eg conv nets, FFNs, transformers, Diffusion etc. I have a little experience with NLP like NLTK and TTS networks. Also some other general dev technologies like ec2,s3,sql,mongoose, etc. That all being said I can’t seem to even get interviews these days just straight rejections not talking to recruiters. On the other hand in 2020, I was just searching for jobs passively and had something like a 75% success rate with getting interviews. I know the job market has changed but I’m a lot more experienced at this time than then and having abysmal luck. Anyone have any advice would be happy to share my resume if that would make it easier to give advice. Also open to hearing what other technologies o should/could learn. submitted by /u/AbjectDrink3276 [link] [comments]
- Arxiv [D]ives - Segment Anythingby /u/FallMindless3563 (Machine Learning) on September 30, 2023 at 3:20 pm
Every Friday for the past few months we’ve been hosting a public paper club called “Arxiv Dives”. We pick a paper and dive deep into it and chat about it as a group. There are a lot of gems of knowledge hidden in these research papers, and the main motivation is simply to keep up with most impactful techniques in the field by taking the time to dive in and discuss. The attendees so far have been great, and would love for anyone is interested to join! https://lu.ma/oxenbookclub submitted by /u/FallMindless3563 [link] [comments]
- [D] What exactly are the compute requirements for training a dense model versus an MoE?by /u/vatsadev (Machine Learning) on September 30, 2023 at 2:00 pm
Hi, New to ML, I can't find a clear answer to this question. I find references online to a 1.8 trillion parameter model taking up the computational power of a 10B model, yet I also hear that the memory requirements a lot higher for an MoE? If I was interested in training/inferencing, for example, a 15M dense model, or a 60M MoE with 4 15M experts. whats the difference gonna be? submitted by /u/vatsadev [link] [comments]
- [D] How to Integrate fine tuned LLAMA 2 in website ?by /u/BookAny3024 (Machine Learning) on September 30, 2023 at 9:36 am
I'm absolute beginner in Machine Learning. Me and My team are building a Chat Bot that recommends medicine based on symptoms, for that we are fine tuning LLAMA 2. Uploading BOOKS to train and we will ask question based on that books. SomeHow I got code on github to FineTune LLAMA 2. But how can I Integrate in my website ? How to connect it in my web app. Need some guidance. We have submission in 2 weeks. If anyone is willing to mentor us in this project or just guide what to do. submitted by /u/BookAny3024 [link] [comments]
- [D] What algorithms to use text classificationby /u/AnyJello605 (Machine Learning) on September 30, 2023 at 7:46 am
I have some data - twitter description of an event in text and the event itself. If I have 100000 tweets in column X and a category in Y - e.g sporting event review, movie review, news, etc what is the best algorithm to match them. Should I make the description a bag of words and depending on the word frequency I can train a ML model (random forest,svm,etc.) or can the algorithm take into account the order. submitted by /u/AnyJello605 [link] [comments]
- [D] Deploy the Mistral 7b Generative Model on an A10 GPU on AWSby /u/juliensalinas (Machine Learning) on September 30, 2023 at 7:11 am
Hello, The Mistral 7b AI model beats LLaMA 2 7b on all benchmarks and LLaMA 2 13b in many benchmarks. It is actually even on par with the LLaMA 1 34b model. So I made a quick video about how to deploy this model on an A10 GPU on an AWS EC2 g5.4xlarge instance: https://nlpcloud.com/deploy-mistral-7b-on-a10-gpu-on-aws.html I hope it will be useful. If you have recommendations about how to improve this video please don't hesitate to let me know, that will be very much appreciated! Julien submitted by /u/juliensalinas [link] [comments]
- [D] CIDEr values in PaLI model and XM 3600 datasetby /u/KingsmanVince (Machine Learning) on September 30, 2023 at 2:46 am
I am reading PaLI: A Jointly-Scaled Multilingual Language-Image Model . In their table 2 (page 6), it's reported that Thapliyal et al. (2022) (0.8B) model got 57.6 of CIDEr on XM 3600 for English. Thapliyal et al. (2022) is Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset. However in this paper, the CIDEr values are reported less than 1. For example, the largest model got 0.584 of CIDEr on XM 3600 for English. Could someone explain to me why those values have great differences? submitted by /u/KingsmanVince [link] [comments]
- [R] Pathway to self-learning mathematics and statistics for ML researchby /u/Far_Clothes_5054 (Machine Learning) on September 30, 2023 at 12:30 am
Hey everyone. I am very passionate about getting in ML research and was wondering what the learning pathway was, particularly with regards to the theoretical Math and Statistics involved. For context: I am a second year undergraduate. By the end of this year, I will have taken and finished A Multivariable Calculus with Proofs course, so that is my current starting point. I have been working with ML for the last 3 years and am proficient in Python and frameworks like PyTorch. I have also made my own implementation of several research papers (LSTMs, GRUs, Transformers, ELMo, BERT, GPT, as well as a few computer vision papers). I have a good general intuition of how deep learning works, but I want to formalise this knowledge with the adequate mathematical background so that I can eventually pursue a career in research. I understand that I have plenty of time until I reach there, and I am willing to dedicate it to grinding out the math and statistical knowledge required. I have done my research on this sub and other forums, and here are a few resources that stood out: Mathematics for Machine Learning by Deisenroth, Faisal and Ong Advanced Calculus of Several Variables by C. H. Edwards Jr. Mathematical Methods Lecture Notes from Imperial College by Deisenroth and Cheraghchi The original information theory paper by Shannon The Elements of Statistical Learning by Hastie, Tibshirani and Friedman Pattern Recognition and Machine Learning by Bishop The Probabalistic Machine Learning Series by Kevin P. Murphy Deep Learning by Goodfellow, Bengio and Courville Mathematics of Machine Learning on MIT OCW (here) My question is, what order should I start self-learning in, given the (somewhat limited) background knowledge I have? Also, are there any other resources that would help? submitted by /u/Far_Clothes_5054 [link] [comments]
- [D] What is the best open-source framework to create a synthetic and domain specific dataset for fine-tuning small models?by /u/Separate-Still3770 (Machine Learning) on September 30, 2023 at 12:18 am
Hi everyone, With the different data points, such as phi-1.5 performance being as good as 7b models on some tasks, it seems to be plausible that small models can be quite capable on specific tasks. I am working on BlindChat, an open-source and private solution to run small LLMs on your browser and I am interested in fine-tuning a phi-1.5 on some domain specific data. I am thinking of having an approach similar to the researchers of the phi paper, which is creating a high quality dataset using GPT3.5 / GPT4. Do you know good open-source frameworks that make it easy to create a high quality data for a specific task using an existing large model, like GPT3.5/4 or Llama 2 70b? submitted by /u/Separate-Still3770 [link] [comments]
- [R] Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelsby /u/MysteryInc152 (Machine Learning) on September 29, 2023 at 10:49 pm
Paper - https://arxiv.org/abs/2307.07162 submitted by /u/MysteryInc152 [link] [comments]
- [Research] - Resource to query ML and LLM based researchby /u/_llama2 (Machine Learning) on September 29, 2023 at 10:00 pm
Made a repo for you all to try using a collaborative AI tool which includes 100+ papers on LLM-Based-Agents. You can try out the repo here: https://www.collama.ai/varun/llm-based-agents submitted by /u/_llama2 [link] [comments]
- [R] Gsgen: Text-to-3D using Gaussian Splattingby /u/Sirisian (Machine Learning) on September 29, 2023 at 8:38 pm
Project Page Paper Code In this paper, we present Gaussian Splatting based text-to-3D generation (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods suffer from inaccurate geometry and limited fidelity due to the absence of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art representation, to address existing shortcomings by exploiting the explicit nature that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under a 3D geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative refinement to enrich details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D content with delicate details and more accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. submitted by /u/Sirisian [link] [comments]
- [P] Carton – Run any ML model from any programming languageby /u/vpanyam (Machine Learning) on September 29, 2023 at 7:28 pm
Hi! I just open-sourced a project that I've been working on for a while and wanted to see what you think! The goal of Carton (https://carton.run) is to let you use a single interface to run any machine learning model from any programming language. It’s currently difficult to integrate models that use different technologies (e.g. TensorRT, Ludwig, TorchScript, JAX, GGML, etc) into your application, especially if you’re not using Python. Even if you learn the details of integrating each of these frameworks, running multiple frameworks in one process can cause hard-to-debug crashes. Ideally, the ML framework a model was developed in should just be an implementation detail. Carton lets you decouple your application from specific ML frameworks so you can focus on the problem you actually want to solve. At a high level, the way Carton works is by running models in their own processes and using an IPC system to communicate back and forth with low overhead. Carton is primarily implemented in Rust, with bindings to other languages. There are lots more details linked in the architecture doc below. Importantly, Carton uses your model’s original underlying framework (e.g. PyTorch) under the hood to actually execute the model. This is meaningful because it makes Carton composable with other technologies. For example, it’s easy to use custom ops, TensorRT, etc without changes. This lets you keep up with cutting-edge advances, but decouples them from your application. I’ve been working on Carton for almost a year now and I open sourced it on Wednesday! Some useful links: Website, docs, quickstart - https://carton.run Explore existing models - https://carton.pub Repo - https://github.com/VivekPanyam/carton Architecture - https://github.com/VivekPanyam/carton/blob/main/ARCHITECTURE.md Please let me know what you think! submitted by /u/vpanyam [link] [comments]
- [D][R] Deploying deep models on memory constrained devicesby /u/jasio1909 (Machine Learning) on September 29, 2023 at 4:14 pm
Suppose we want to use a deep learning model on a gpu within our app. We want this model to coexist on the gpu with other processes, effectively limit it's possible usage of resources. As cuDNN/cuBLAS routines are nondeterministic and possibly dynamically allocate variable amount of memory, how do people manage this problem? Is it a problem at all? Estimating memory usage of deep learning models on gpu is notoriously hard. There is a research paper from Microsoft tackling this problem and they mispredict the usage of memory by 15% on average. Some cpu BLAS libraries like openBLAS or MKL also dynamically allocate the memory, but there are alternatives - LAPACK as far as I know uses only the memory provided by the caller, making it viable option for applications in embedded. In safety criticall tasks like autonomous driving, it seems to be especially important to have deterministic and clear bounds on memory usage of the process and not get spontaneously hit by CUDA OOM error. I can imagine that for autonomous vehicles, the prediction pipeline usually is the only process occupying the GPU, making the problem less visible or go away completely. In case of desktop applications only running the inference, the problem is also less visible as the memory requirements for forward pass only are comparatively low (we can reuse allocated memory blocks efficiently). However, I am looking on this subject through the problem of training/finetuning deep models on the edge devices, being increasingly available thing to do. Looking at tflite, alibaba's MNN, mit-han-lab's tinyengine etc.. To summarize: 1. Do nondeterministic memory allocations pose a problem for deploying deep models in the wild and if so, what strategies do people employ to mitigate this problem? 2. Do you think it would be beneficial to have a deep learning library with worse performance but with fine graned controll over the memory allocations? (If such library doesn't already exist. If it does, please tell me.) Such a library could possibly enable you to choose from a list of possible computation routines, providing you with required memory before the call is made and choose suitable perf/memory tradeoff routine for a given state of the machine per function call. Eg: if os.free_mem>matmul(x,y,fast).mem_cost: matmul(x,y,fast).compute() else: matmul(x,y,economic).compute() submitted by /u/jasio1909 [link] [comments]
- [R] RealFill: Reference-Driven Generation for Authentic Image Completionby /u/StrawberryNumberNine (Machine Learning) on September 29, 2023 at 1:42 pm
Project page: https://realfill.github.io/ Paper: https://arxiv.org/abs/2309.16668 RealFill is able to complete the image with what should have been there. Abstract Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions, but the content these models hallucinate is necessarily inauthentic, since the models lack sufficient context about the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. submitted by /u/StrawberryNumberNine [link] [comments]