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.
- [P] I made a browser extension that remove link Google Search Consoleby /u/GaylordTurner (Machine Learning) on February 6, 2023 at 5:25 am
submitted by /u/GaylordTurner [link] [comments]
- [R] Creating a Large Language Model of a Philosopherby /u/starstruckmon (Machine Learning) on February 6, 2023 at 4:22 am
Paper : https://arxiv.org/abs/2302.01339 Abstract : Can large language models be trained to produce philosophical texts that are difficult to distinguish from texts produced by human philosophers? To address this question, we fine-tuned OpenAI's GPT-3 with the works of philosopher Daniel C. Dennett as additional training data. To explore the Dennett model, we asked the real Dennett ten philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry-picking. We recruited 425 participants to distinguish Dennett's answer from the four machine-generated answers. Experts on Dennett's work (N = 25) succeeded 51% of the time, above the chance rate of 20% but short of our hypothesized rate of 80% correct. For two of the ten questions, the language model produced at least one answer that experts selected more frequently than Dennett's own answer. Philosophy blog readers (N = 302) performed similarly to the experts, while ordinary research participants (N = 98) were near chance distinguishing GPT-3's responses from those of an "actual human philosopher". submitted by /u/starstruckmon [link] [comments]
- [D] Yann Lecun seems to be very petty against ChatGPTby /u/supersoldierboy94 (Machine Learning) on February 6, 2023 at 3:22 am
I know Lecun's fanbois are gonna go after me but here it goes... I get that he is one of the godfathers of AI. Mostly on the research side which immediately puts him very hostile against engineers. But I guess it is understandable given the fact that he works on Meta and Meta has faced a lot of backlash (for good and bad reasons), most especially with Galactica where their first rollout got so bad they had to close it immediately. It's also particularly funny given his political leaning that he is very spiteful of a company that uses open-source knowledge and builds on top of it. Lately, his social media and statements are barrages against ChatGPT and LLM's. Sure, he may have a point here and there but his statements look very petty. Here are some examples "By releasing public demos that, as impressive & useful as they may be, have major flaws, established companies have less to gain & more to lose than cash-hungry startups. If Google & Meta haven't released chatGPT-like things, it's not because they can't. It's because they won't." > Except that anyone in the IT industry knows that big tech companies cant release something very fast because of politicking and bureaucracy in the system. It takes years to release something into public in big tech compared to startups. "Data on the intellectual contribution to AI from various research organizations. Some of organizations publish knowledge and open-source code for the entire world to use. Others just consume it." > Then adds a graph where the big tech is obviously at the top of the race for most number of AI-related research papers (without normalizing it to the number of researchers per org) "It's nothing revolutionary, although that's the way it's perceived in the public," the computer scientist said. "It's just that, you know, it's well put together, it's nicely done." > Except that it is indeed revolutionary in terms of the applied research framework -- adding on top of open-source, state-of-the-art research and quickly putting it into production for people to use. "my point is that even the engineering work isn't particularly difficult. I bet that there will be half a dozen similar similar systems within 6 months. If that happens, it's because the underlying science has been around for a while, and the engineering is pretty straightforward." "I'm trying to correct a \perception* by the public & the media who see chatGPT as this incredibly new, innovative, & unique technological breakthrough that is far ahead of everyone else.* It's just not." "One can regurgitate Python code without any understanding of reality." "No one is saying LLMs are not useful. I have forcefully said so myself, following the short-lived release of FAIR's Galactica. People crucified it because it could generate nonsense. ChatGPT does the same thing. But again, that doesn't mean they are not useful." He also seems to undermine the rapid engineering work and MLOps that come with ChatGPT which is funny because Meta hasn't released any substantial product from their research that has seen the light of the day for a week. Also, GPT3 to ChatGPT in itself in a research perspective is a jump. Maybe not as incremental as what Lecun does every paper, but compared to an average paper in the field, it is. To say that LLMs are not intelligent and it just regurgitates Python code probably haven't used CoPilot, for example. It's a classic case of a researcher-engineer beef. And that a startup can profit from derivatives of research that big tech has published. OpenAI broke their perspective on the profit from research. Big tech tried to produce revolutionary research papers on a surplus but never puts them into production thinking that they are the only companies that could if they want to. Then once one company created a derivative of a large research work and profited from it, it baffled them. Although people could argue that Stable Diffusion did this first in the Generative Image Space. It's one thing to correct misconceptions in the public. It's also one thing not to be petty about the overnight success of a product and an immediate rise of a company that got embraced warmly by tech and non-tech people. It's petty to gatekeep. At the end of the day, ML is not just about research, it's applied research. It's useless until it reaches the end of the tunnel. 99% of research papers out there are just tiny updates over the state of the art which has been a pointless race for about a year or two, with no reproducible code or published data. Inventing combustion engine is just as important as putting it in the car. submitted by /u/supersoldierboy94 [link] [comments]
- [D] Open Source Implementation of Dialogue LLMs like ChatGPT with Reinforcement Learning from Human Feedback?by /u/itisyeetime (Machine Learning) on February 6, 2023 at 3:11 am
Looking at the writeups on ChatGPT seems to indicate that part of improvements is the human feedback through reinforcement learning(a human "ranks" multiple generated response, and from the ranking, a reward is calculated). Interestingly enough, this important seems to have originated in InstructGPT. My question is do any open source implementation exists of a InstructGPT or ChatGPT-like system where human feedback is used to help "guide" the training of a large language model? submitted by /u/itisyeetime [link] [comments]
- [P] I Made a Text Bot Powered by ChatGPT, DALLE 2, and Wolfram Alphaby /u/ImplodingCoding (Machine Learning) on February 6, 2023 at 1:51 am
submitted by /u/ImplodingCoding [link] [comments]
- [R] deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMGby /u/t0ns0fph0t0ns (Machine Learning) on February 6, 2023 at 1:45 am
submitted by /u/t0ns0fph0t0ns [link] [comments]
- [D] AtheneWins just showcased an AI streamer bot, Does anyone know how he did this?by /u/imagoons (Machine Learning) on February 6, 2023 at 12:25 am
submitted by /u/imagoons [link] [comments]
- [D] RNN and S4 etcby /u/windoze (Machine Learning) on February 6, 2023 at 12:20 am
Hello what's the state of modern RNNs, why does S4 not use nonlinearity on the state vector? What happened to unitary RNN or independent RNN (which sounds like exponential moving average)? submitted by /u/windoze [link] [comments]
- [D] Overview of of Chatbot Research?by /u/renbid (Machine Learning) on February 5, 2023 at 11:46 pm
Is there a good overview of the state of chatbot research? I'm wondering if the ChatGPT approach of big LLM + RLHF is now considered the only way forward? How about alternatives like BlenderBot3? And what are the best open source chatbots right now? Or if you can't create your own ChatGPT, how does using a GPT3 sized model + prompt engineering compare to smaller models with supervised fine tuning on a conversation dataset? submitted by /u/renbid [link] [comments]
- [D] Large language models (LLM) as priority / conflict resolver for embodied AI or in generalby /u/projekt_treadstone (Machine Learning) on February 5, 2023 at 11:43 pm
I wanted to discuss the possibilities to use LLM in generating answer based on the context and resolving conflict. Some recent work leveraging LLM in robotics planning, like Language Models as Zero-Shot Planner use LLM to generate plans for robot. What are your views in terms of LLM which leverage the background knowledge and visual clues together to generate correct next action by robots or embodied systems. As a human we decide actions based on resolving priority or conflict based on rules/ concepts , can LLM takes these rules /concept explicitly in decision making to generate new set of actions? Example: while chopping the veggies by robots, if hand comes in between then robot will stop the chopping process of veggies. As chopping task and human hand presence are in conflict and humans hand safety is of higher priority than cutting. How such small-small kind of knowledge be encoded in these robotics system which makes them more safer and trustworthy in general. As LLM requires larges corpus of knowledge/data. submitted by /u/projekt_treadstone [link] [comments]
- [D] Is there a database of English language tokens, including all dictionary words and common word segments?by /u/MrOfficialCandy (Machine Learning) on February 5, 2023 at 11:41 pm
I find it odd that I have to regenerate this from my input set each time. It should be something we can just start with pre-created. submitted by /u/MrOfficialCandy [link] [comments]
- [D] Is English the optimal language to train NLP models on?by /u/MrOfficialCandy (Machine Learning) on February 5, 2023 at 11:13 pm
While the greatest amount of training content is available for English at the moment, it seems unlikely to me that it's an efficient language to train AI. A more optimal language would reduce training time and model size. It might, for example, be much more efficient to train AI on Chinese, Korean, or Japanese due to a reduce grammatical token-set when constructing sentences/ideas. But taking the idea further, I wonder if we should be using a human language at all. Perhaps it's more efficient to use something altogether new in order to both communicate with AI more exactingly and also to reduce model size/training. What do y'all think? submitted by /u/MrOfficialCandy [link] [comments]
- [R] BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelsby /u/Illustrious_Row_9971 (Machine Learning) on February 5, 2023 at 10:34 pm
submitted by /u/Illustrious_Row_9971 [link] [comments]
- [P] Interactive Map of NeurIPS Proceedings 1987-2022by /u/NomicAI (Machine Learning) on February 5, 2023 at 10:09 pm
submitted by /u/NomicAI [link] [comments]
- [P] I made CoPilot for writing LaTeX (in Overleaf) - what do you think?by /u/alistairmcleay (Machine Learning) on February 5, 2023 at 9:40 pm
submitted by /u/alistairmcleay [link] [comments]
- [P] NeuralFit now allows evolution of recurrent neural networksby /u/wagenaartje (Machine Learning) on February 5, 2023 at 8:25 pm
Hi all! Some time ago I made a post about NeuralFit (https://github.com/neural-fit/neuralfit), which allows you to evolve neural networks in 🐍 Python with just a few lines of code. Good news: it is now possible to evolve recurrent models on timeseries, which is useful for doing stock market predictions for example 📈. There is currently a simple example, but more examples will be added soon! In addition: the free limitations have been increased, so you can use NeuralFit for most hobby projects without needing a license. Just like last time, feedback is immensely appreciated! submitted by /u/wagenaartje [link] [comments]
- [R] Can anyone direct me to academic sources arguing that Big Tech using AI for targeted Social Media ads is a good thing for actual users?by /u/lara_lara24 (Machine Learning) on February 5, 2023 at 7:44 pm
Been struggling to find sources relating to this, it’s mostly just tech websites or blogs I keep coming across. I’m struggling to find any academic papers arguing for specifically the use of user data to create targeted ads. submitted by /u/lara_lara24 [link] [comments]
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submitted by /u/DenofBlerds [link] [comments]
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