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.

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:

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  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.

  • [N] Feds appoint “AI doomer” to run US AI safety institute
    by /u/bregav (Machine Learning) on April 17, 2024 at 10:49 pm

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  • [D] Is Risk Aversion Crushing the Adoption of Cloud Abstractions?
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  • [Discussion] PhD in Statistics Job Prospects
    by /u/SpiritualCellist4303 (Machine Learning) on April 17, 2024 at 8:59 pm

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  • [D] Is there a way to determine if the representations a model learns are spherical or hyperbolic?
    by /u/Mad_Scientist2027 (Machine Learning) on April 17, 2024 at 8:49 pm

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  • [R] RuleOpt: Optimization-Based Rule Learning for Classification
    by /u/zedeleyici3401 (Machine Learning) on April 17, 2024 at 7:34 pm

    Paper: https://arxiv.org/abs/2104.10751 Package: https://github.com/sametcopur/ruleopt Documentation: https://ruleopt.readthedocs.io/ RuleOpt is an optimization-based rule learning algorithm designed for classification problems. Focusing on scalability and interpretability, RuleOpt utilizes linear programming for rule generation and extraction. The Python library ruleopt is capable of extracting rules from ensemble models, and it also implements a novel rule generation scheme. The library ensures compatibility with existing machine learning pipelines, and it is especially efficient for tackling large-scale problems. Here are a few highlights of ruleopt: Efficient Rule Generation and Extraction: Leverages linear programming for scalable rule generation (stand-alone machine learning method) and rule extraction from trained random forest and boosting models. Interpretability: Prioritizes model transparency by assigning costs to rules in order to achieve a desirable balance with accuracy. Integration with Machine Learning Libraries: Facilitates smooth integration with well-known Python libraries scikit-learn, LightGBM, and XGBoost, and existing machine learning pipelines. Extensive Solver Support: Supports a wide array of solvers, including Gurobi, CPLEX and OR-Tools. submitted by /u/zedeleyici3401 [link] [comments]

  • [D] LSTM Time Series Forecasting
    by /u/StressAccomplished26 (Machine Learning) on April 17, 2024 at 7:15 pm

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  • [R] ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
    by /u/SeawaterFlows (Machine Learning) on April 17, 2024 at 5:49 pm

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    by /u/SeawaterFlows (Machine Learning) on April 17, 2024 at 5:34 pm

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  • [D] Question: Time-series decoding to non-temporal latent space?
    by /u/reesespike (Machine Learning) on April 17, 2024 at 5:08 pm

    Hello! I am a researcher in computational neuroscience, looking to apply some contemporary machine learning techniques to fMRI timeseries data. I have a collection of highly dimensional 4D fMRI timeseries data collected while subjects were observing naturalistic images from COCO at regular intervals. We currently have decoding models that take preprocessed "snapshots" of this timeseries data flattened into an activation pattern that is aggregated over the short period the image was being observed, and use some machine learning models to decode and reconstruct the image content from the brain. (See some of my recent work). I am curious what sort of machine learning techniques exist that might be able to address the time-series data itself, without having to collapse the timeseries to a single snapshot to perform our decoding process. What I am envisioning is a model (perhaps a transformer) that can take as input a highly dimensional multichannel timeseries and output a flattened latent representation (say, a CLIP vector) corresponding to an image stimulus, or even a series of latent vectors separated by a known regular interval (as we have in our data for the different image presentations). To my knowledge most of the work in machine learning with time series data is in forecasting, but what I want is a static (or potentially repetitive) output. My hope is that the more detailed timeseries data will have additional signal that will boost decoding performance for fMRI vision decoding. Is there any existing work in the field of ML that has tackled a similar problem? submitted by /u/reesespike [link] [comments]

  • [D] Microsoft AutoML for ML.NET with DirectML
    by /u/tradingnumbers (Machine Learning) on April 17, 2024 at 4:13 pm

    I have built a model for detecting outliers in a data series using ML.NET. I read from the dev forums that ML.NET using DirectML can support the new NPUs built into the new Core Ultra processors from Intel. I have not been able to find evidence that this is true for AutoML from the Microsoft team. Does anyone have experience using AutoML with DirectML backend? submitted by /u/tradingnumbers [link] [comments]

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    by /u/shuvamg007 (Machine Learning) on April 17, 2024 at 2:49 pm

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  • [D] How does visual embedding coexist with language embedding space in Vision Language Model?
    by /u/E-fazz (Machine Learning) on April 17, 2024 at 2:41 pm

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  • Good Resources on Time Series Forecasting? [D]
    by /u/secret_fyre (Machine Learning) on April 17, 2024 at 2:26 pm

    Can anyone recommend any good resources on modern time series forecasting with machine learning? I found one book on time series forecasting on Amazon with great reviews called Time Series Forecasting in Python. Having said that, a lot of machine learning books and resources seem to gloss over time series. What are some good resources (either entire books, or chapters in books) that cover time series? submitted by /u/secret_fyre [link] [comments]

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    Hi I am aware that a lot of transformer encoder variations exist (BERT, DistilBERT, Deberta, Roberta ...). However I am not interested in the best ones (that should probably be Deberta V3) but rather the ones that can quickly have decent results even with very few example examples (like ~50,100 sentences each containing maybe 1, 2 or 3 entities). I have done a few experiments in english, and to my surprise it seems that the one that perform best with as few data as possible is the original english BERT model (google-bert/bert-base-uncased on HF), and not one of the more recent variations. I have also done other experiments in french, and the multilingual BERT also quickly get decent results faster than models specially trained on french data (e.g CamemBERT). The models I've compared include : bert, bert multilingual, distilbert, distilbert multilingual, roberta, xlm-roberta, camembert, camemberta, distilroberta, debertav3, debertav3 multilingual What are your thought about this ? Is it something surprising or unusual ? Any advice ? submitted by /u/LelouchZer12 [link] [comments]

  • Word embedding - contextualised vs word2vec [D]
    by /u/datashri (Machine Learning) on April 17, 2024 at 1:03 pm

    Noob question about word embeddings - As far as I understand so far - Contextualized word embeddings generated by BERT and other LLM type models use the attention mechanism and take into account the context of the word. So the same word in different sentences can have different vectors. This ^ is opposed to the older approach of models like word2vec - embeddings generated by word2vec are not contexual. However, looking closely at the CBOW and skip-gram models. it seems that they too try to predict the central word based on the surrounding (context) words. So the embeddings generated by word2vec can also be contextual. So they're both contexutal? What am I missing? submitted by /u/datashri [link] [comments]

  • [D] What is the modern Approach to Speaker Verification?
    by /u/Puzzleheaded_Bee5489 (Machine Learning) on April 17, 2024 at 12:39 pm

    By modern I mean any new innovation in the field of Speaker Verification. I was researching more about ML in the field of Audio - Speech in particular and I notice there are so many things going on right now with LLM being integrated into almost everything. So I was curious to know if there is any new innovation in the field of Speaker Recognition. Some of the cool libraries I came across were - pyannote.audio, speechbrain, Nvidia NeMo which provide the framework and pre-trained models for the task of Speaker Verification. Thanks in advance! ​ submitted by /u/Puzzleheaded_Bee5489 [link] [comments]

  • [D] hyperparameter tuning, learn or not learn at all?
    by /u/FFFFFQQQQ (Machine Learning) on April 17, 2024 at 12:30 pm

    I have been doing some fine tuning work, and I am adjusting the weight decay and learning rate of my transformer models. My base model is BERT, and the fine tune data set is quite small. The issue I had was when I set incorrect hyperparameters, the model do not do anything. For example, if the optimal learning rate is 5e-3, but I am tuning it using 1e-2, 1e-3, 1e-4. Then the F measures are all 0.0. I understand the hyperparameter affects the results a lot. But I didn't expect it to be learn or not learn at all. I wonder if it is normal. cause 5e-3 and 1e-3 is not that much difference? submitted by /u/FFFFFQQQQ [link] [comments]

  • [Discussion]ACM MM2024
    by /u/INeedPapers_TTT (Machine Learning) on April 17, 2024 at 10:35 am

    This is the first year (if I remember correclty) that MM shifts from CMT to Openreview. As an author I've been sensing something wrong since I created my submission, i.e. desk rejection even before abstract ddl, inconsistency about whether to include submission number within the paper, etc. Now I've heard a lot from social media that many authors without many/any publications (yes including me) have been nominated as reviewers due to their lack of reviewers for the submission volume. I'm very concerned about the quality of the reviews and the submission in MM2024 this year. submitted by /u/INeedPapers_TTT [link] [comments]

  • Time-series forecasting on batch process [P]
    by /u/Bitter__Physics (Machine Learning) on April 17, 2024 at 10:21 am

    I am currently working on a fed batch process and I need to know if time series can be achieved with good accuracy. The idea is that there is a set of differential equations which create the data for me. After this data, a model is created with high accuracy. The question is, is it possible to achieve a time series prediction by giving the model a complete different set of initial conditions? My job is to have this model predict in completely different initial conditions so there is no need for real life testing on the batch process but just computational. I was looking into Neural ODE, UDE etc. in order for the model to understand the dynamics but I am also not sure if other methods of time series would work. (The data has no periodicity, correlated between each other etc.) What do you think would be the best approach since I am constantly trying different methods but none give accurate results? submitted by /u/Bitter__Physics [link] [comments]

  • [D] What comes first, math, or algorithm in research?
    by /u/Deep-Station-1746 (Machine Learning) on April 17, 2024 at 8:22 am

    I'm learning meths behind diffusion right now (DDPM, Score-based, and other approaches). I'm wondering how exactly did researchers come up with the idea? Does inventing new approaches go something like this? 1. We want to make better image generator. 2. Oh, the data will never be enough... 3. Let's multiply data - by adding some noise corruption 4. This this works well, what if we make a denoising network? 5. What if we make network that makes an image from pure noise? 6. That doesn't work, what if we did smaller denoising steps? 7. This works! Now, let's create some theory on why it works. 8. Write the paper Or something like this? 1. We want to make better image generator. 2. We know "nonequilibrium thermodynamics" really well and want to try applying it somehow 3. We somehow come up with an algorithm that relies on math from that theory 4. It works! 5. We write the paper. Which comes first usually? Math or Algorithm? submitted by /u/Deep-Station-1746 [link] [comments]

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