What is the Best Machine Learning Algorithms for Imbalanced Datasets

Machine Learning Algorithms and Imbalanced Datasets

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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
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.

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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.


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    by /u/abstractcontrol (Machine Learning) on January 30, 2023 at 4:47 pm

    In order to improve my talking skills, I am doing a little series on how to setup Stable Diffusion on Paperspace, and I am astounded how much time it takes to do the audio editing. Well, part of the reason is that I've only been doing this for 3 days and my process is very inefficient, but it feels that in the current time, neural nets should be able to do things like remove uhms, lip smacking and breath intakes. I've looked around, and this post from 9 years ago says the only choice is to edit it by hand. Is that still true? submitted by /u/abstractcontrol [link] [comments]

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    I've been working in the speech and voice space for a while now and am now building out some tooling in the space to make it easier for researchers/engineers/developers to build speech processing systems and features; I'd love to hear what people in ML struggle with when you're trying to build or work with speech processing for your projects/products (beyond speech-to-text APIs) submitted by /u/jiamengial [link] [comments]

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    I am a researcher at a US university and have a budget of 25k to build a PC for training various ML algorithms (e.g. DRL, neuromorphic computing, VAE, etc). I'm trying to decide between going for prebuilds (like https://lambdalabs.com/gpu-workstations/vector) or building with consumer cards like 4090s. Any advice on which is the most bang for the price? Im not sure how much Im giving up by going for consumer 24g cards vs a6000, 6000 ada but prebuild prices go up quick. Warrantee vs building it myself isn't an issue submitted by /u/seanrescs [link] [comments]

  • [R] Parsel: A (De-)compositional Framework for Algorithmic Reasoning with Language Models - Stanford University Eric Zelikman et al - Beats prior code generation sota by over 75%!
    by /u/Singularian2501 (Machine Learning) on January 30, 2023 at 2:06 pm

    Paper: https://arxiv.org/abs/2212.10561 Github: https://github.com/ezelikman/parsel Twitter: https://twitter.com/ericzelikman/status/1618426056163356675?s=20 Website: https://zelikman.me/parselpaper/ Code Generation on APPS Leaderboard: https://paperswithcode.com/sota/code-generation-on-apps Abstract: Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs. For these tasks, humans often start with a high-level algorithmic design and implement each part gradually. We introduce Parsel, a framework enabling automatic implementation and validation of complex algorithms with code LLMs, taking hierarchical function descriptions in natural language as input. We show that Parsel can be used across domains requiring hierarchical reasoning, including program synthesis, robotic planning, and theorem proving. We show that LLMs generating Parsel solve more competition-level problems in the APPS dataset, resulting in pass rates that are over 75% higher than prior results from directly sampling AlphaCode and Codex, while often using a smaller sample budget. We also find that LLM-generated robotic plans using Parsel as an intermediate language are more than twice as likely to be considered accurate than directly generated plans. Lastly, we explore how Parsel addresses LLM limitations and discuss how Parsel may be useful for human programmers. https://preview.redd.it/66zehsdps6fa1.jpg?width=811&format=pjpg&auto=webp&s=0da18699f4176abe5319a76c27bb71e6b0728e4b https://preview.redd.it/is4pzwdps6fa1.jpg?width=1638&format=pjpg&auto=webp&s=d07aba27a117425e4cd54fa08e0bf4bbccc356a9 https://preview.redd.it/szkbb0eps6fa1.jpg?width=711&format=pjpg&auto=webp&s=a0992345b2a717c1439b44186887aad5db9c3f51 https://preview.redd.it/6lk1wzdps6fa1.jpg?width=1468&format=pjpg&auto=webp&s=6e28cbfd39b45e54bf75b382a6a143f7edd5d46c https://preview.redd.it/8h7p8vdps6fa1.jpg?width=1177&format=pjpg&auto=webp&s=6366027b77dcb8fc925f56318614eca0fae21496 submitted by /u/Singularian2501 [link] [comments]

  • [P] Keras model production deployment
    by /u/ProfessionalOverall8 (Machine Learning) on January 30, 2023 at 1:02 pm

    Hi guys. It's been some time since I started developing my Keras models, but now is the first time I am trying to push it to production. My Keras model looks like this: model = Sequential() model.add(Bidirectional(LSTM(256, return_sequences=True))) model.add(Bidirectional(LSTM(256, return_sequences=True))) model.add(TimeDistributed(Dense(1, activation='sigmoid'))) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) My problem is I need to run through about 25 of these for every written sentence. There is going to be an online editor, where users can paste text for my analysis. That means up to about 300 words or about 20 sentences at once. With the current time to run each network (about 0.2s), that means 25 * 0,2 * 20 or about 100s per user input. I am going for 30 seconds at most with potentially dozens of users at once. Ideally on a Raspberry Pi 4. The internet is surely gonna back me up I thought to myself and started googling. If only I know what kind of a rabbit hole I was about to fall into. First I converted my Keras model into a TensorFlow frozen graph model. 10x time improvement on CPU, but still at 0.2s on average. Another thing I think may boost the performance is retraining the models for variable input shape (currently I always feed in 50 values). With the average sentence size of 16 words this may, from what I understand, lead to a 3 times boost? My question is: now what? What can I do to make it faster? Is it even possible to run it on a Raspberry Pi 4 and get reasonable response times? If not, what is my best option on a tight budget? submitted by /u/ProfessionalOverall8 [link] [comments]

  • [D] I want to understand the broad steps for building something like Adept.AI
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  • [Discussion] ChatGPT and language understanding benchmarks
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    The general consensus seems to be that large language models, and ChatGPT in particular, have a problem with accuracy and hallucination. As compared to what, is often unclear, but let's say as compared to other NLP methods of question answering, language understanding or as compared to Google Search. I haven't really been able to find any reliable sources documenting this accuracy problem, though. The SuperGLUE benchmark has GPT-3 ranked #24, not terrible, but outperformed by old models like T5, which seems odd. GLUE nothing. SQUAD nothing. So, I'm curious: Is there any benchmark or metric reflecting the seeming step-function made by ChatGPT that's got everyone so excited? I definitely feel like there's a difference between gpt-3 and chatGPT, but is it measurable or is it just vibes? Is there any metric showing ChatGPT's problem with fact hallucination and accuracy? Am I off the mark here looking at question-answering benchmarks as an assessment of LLMs? Thanks submitted by /u/mettle [link] [comments]

  • [D]Are There Studies on text-davinci-003's Zero/Few-shot Performance on Various Academic Benchmarks?
    by /u/gamerx88 (Machine Learning) on January 30, 2023 at 10:15 am

    Has anyone come across studies on GPT3 text-davinci-003's zero/few-shot performance over various NLP benchmarks and how they compare to current SoTA? E.g GLUE, SuperGLUE and over more classic ones like CoNLL 2003 NER. I thought it would be pretty interesting to see how far zero/few-shot learning with LLM has progressed with RLHF and instruction tuning. Am surprised that nobody has done such a benchmark yet. submitted by /u/gamerx88 [link] [comments]

  • [D] Sparse Ridge Regression
    by /u/antodima (Machine Learning) on January 30, 2023 at 9:40 am

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  • [R] A Robust Hypothesis Test for Tree Ensemble Pruning
    by /u/asi_dm (Machine Learning) on January 30, 2023 at 5:25 am

    I'm looking for help/feedback with this paper. Please let me know if the method is interesting and if there's ways to improve it! https://arxiv.org/abs/2301.10115 Abstract: Gradient boosted decision trees are some of the most popular algorithms in applied machine learning. They are a flexible and powerful tool that can robustly fit to any tabular dataset in a scalable and computationally efficient way. One of the most critical parameters to tune when fitting these models are the various penalty terms used to distinguish signal from noise in the current model. These penalties are effective in practice, but are lacking in robust theoretical justifications. In this paper we develop and present a novel theoretically justified hypothesis test of split quality for gradient boosted tree ensembles and demonstrate that using this method instead of the common penalty terms leads to a significant reduction in out of sample loss. Additionally, this method provides a theoretically well-justified stopping condition for the tree growing algorithm. We also present several innovative extensions to the method, opening the door for a wide variety of novel tree pruning algorithms. submitted by /u/asi_dm [link] [comments]

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  • [R] Incorrect Ranking of Vessel Segmentation Algorithms
    by /u/AttilaFazekas (Machine Learning) on January 29, 2023 at 9:01 pm

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  • [D] Simple Questions Thread
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  • [P] Automating a Youtube Shorts channel with Huggingface Transformers and After Effects
    by /u/Ch1nada (Machine Learning) on January 29, 2023 at 3:54 pm

    I’ll try to get into detail about the implementation and difficulties in case it is useful for anyone else trying to do something similar with an applied ML project, so there’s a TLDR at the end if you’d like the short version/result. At the end of last year I convinced myself to start 2023 by creating a side-project that I'd actually finish and deploy and perhaps earn some “passive” income (spoiler, not so passive after all :P), and after some brainstorming I settled on making an automated Youtube channel about finance news since I had just gotten into investing. Shorts seemed to be more manageable and monetization is changing in February so I went with that. My rough initial idea was to get online articles, summarize them, make a basic compilation with some combination of pymovie, opencv and stock photos and done. I was pretty worried about the summarization, since in my ML day job I mainly work with vision or sensor data in manufacturing not NLP. Also, I quickly realized pymovie with still images and some overlayed text was not very attractive for viewers (starting with myself). Fast-forward a few days, and after some research online I came across two things, Huggingface transformers (yep, I know I’ve been living under a rock :P) and After Effects scripting. From here, it became mainly about figuring out exactly which ML models I needed to fine-tune for finance / social media and for what, then putting it all together. The entire workflow looks something like this: the bot fetches online daily news about a topic (stocks or crypto), then sentiment analysis is performed on the title and the full text is summarized into a single sentence. I fine-tuned SBERT on ~1.5M posts from /r/worldnews publicly available in Google Cloud BigQuery so that it could predict a “social engagement” score that could be used to rank and filter the news that would make it into the video. Finally, all of this is combined into a single JSON object written into a .js file that can be used by another “content creator” script to render the video from a template using aerender in Python. The content of this template is generated dynamically based on the contents of the .js file via AE Expressions. This module also uses the TTS lib to generate voice-overs for the text, and is also responsible for generating the title (using NLTK to identify the main subjects of each title) and the video’s description. Pexel stock videos are used for the background. In principle automating the upload to Youtube could also be done, but at this stage I’m handling this manually as the JSON generation is not as robust as I’d like, so the output file often needs to be tweaked and fixed before the video can be finalized and uploaded. An examples is the summary being too short or vague when taken out of the context of the original article. If you increase the max_length of the summarizer to compensate, it can easily become too long to for the overlay to fit the pre-defined dimensions, or the total audio length can be too long for the max duration of a youtube short. With some more work I’m confident the whole process can be automated further. For those interested, feel free to check the result here: Byte Size Bot channel If you have any questions or suggestions I’d be happy to hear them. TLDR: Coded an automated (not 100% yet, but will get there) Youtube Shorts channel about finance news to create a passive income stream. Ended up being way harder, more fun and not so “passive” than my initial expectations. submitted by /u/Ch1nada [link] [comments]

  • [D] GPT-Index vs Langchain
    by /u/TikkunCreation (Machine Learning) on January 29, 2023 at 2:42 pm

    Someone I work with wrote the below for our internal team (shared with permission) and I thought some here may find it helpful. Recently, I built an app that uses GPT-Index & LangChain to provide an answer to a question based on a piece of text as context. I found GPT-Index to be much easier and straightforward to integrate, but it seems like LangChain has more features and is more powerful. Here's my experience integrating both of them. GPT-Index First thing I did was review their docs to make sure I understood what GPT-Index was, what it could do, and how I was going to use it I went back and forth a couple times figuring out how I was going to use it. Then I found the quickstart guide It seemed like the quickstart guide would work so I followed the guide and after a few tries, I was getting solid responses to the questions I asked it LangChain I followed the same step, reviewing their docs. LangChain's docs has more to it because it seems like it does more, so this step took longer It was tough for me to figure out how I needed to use LangChain. I had to ask for some help to better understand our use case Once I thought I knew how I was going to use LangChain, I began coding. I ran into more errors with LangChain It seems that my first approach wasn't correct, so I switched to something similar and I was finally getting a response. The response was 'I don't know'... I didn't know what to do about it Then I checked out the logs of the data being passed through and found that the context was being cut off. To make sure it worked, I asked a question relating to the text that was getting passed through. The response seemed to make sense, so now I know better where the issue is at I still need to fix the context being cut off. I followed the docs of LangChain very closely, so I'm wondering if the docs are old or if I have the wrong implementation So overall, if GPT Index solves your use case, start with that. We also did a variant built without GPT-Index and without langchain, and that one worked well too. We occasionally share stuff like this on genainews.org though the newsletter is mostly about new ai startups and products so figured it good to post here. Anyone else here that's worked with both GPT-Index and langchain and can offer additional thoughts? submitted by /u/TikkunCreation [link] [comments]

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