What are some ways to increase precision or recall in machine learning?

What are some ways to increase precision or recall in machine learning?

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

What are some ways to increase precision or recall in machine learning?

What are some ways to Boost Precision and Recall in Machine Learning?

Sensitivity vs Specificity?


In machine learning, recall is the ability of the model to find all relevant instances in the data while precision is the ability of the model to correctly identify only the relevant instances. A high recall means that most relevant results are returned while a high precision means that most of the returned results are relevant. Ideally, you want a model with both high recall and high precision but often there is a trade-off between the two. In this blog post, we will explore some ways to increase recall or precision in machine learning.

What are some ways to increase precision or recall in machine learning?
What are some ways to increase precision or recall in machine learning?


There are two main ways to increase recall:

by increasing the number of false positives or by decreasing the number of false negatives. To increase the number of false positives, you can lower your threshold for what constitutes a positive prediction. For example, if you are trying to predict whether or not an email is spam, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in more false positives (emails that are not actually spam being classified as spam) but will also increase recall (more actual spam emails being classified as spam).

Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6
Get 20% off Google Workspace (Google Meet)  Business Plan (AMERICAS) with  the following codes:  C37HCAQRVR7JTFK Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

To decrease the number of false negatives,

you can increase your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in fewer false negatives (actual spam emails not being classified as spam) but will also decrease recall (fewer actual spam emails being classified as spam).

What are some ways to increase precision or recall in machine learning?

There are two main ways to increase precision:

by increasing the number of true positives or by decreasing the number of true negatives. To increase the number of true positives, you can raise your threshold for what constitutes a positive prediction. For example, using the spam email prediction example again, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in more true positives (emails that are actually spam being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).

To decrease the number of true negatives,

you can lower your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example once more, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in fewer true negatives (emails that are not actually spam not being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)
What are some ways to increase precision or recall in machine learning?

To summarize,

there are a few ways to increase precision or recall in machine learning. One way is to use a different evaluation metric. For example, if you are trying to maximize precision, you can use the F1 score, which is a combination of precision and recall. Another way to increase precision or recall is to adjust the threshold for classification. This can be done by changing the decision boundary or by using a different algorithm altogether.

What are some ways to increase precision or recall in machine learning?

Sensitivity vs Specificity

In machine learning, sensitivity and specificity are two measures of the performance of a model. Sensitivity is the proportion of true positives that are correctly predicted by the model, while specificity is the proportion of true negatives that are correctly predicted by the model.

Google Colab For Machine Learning

State of the Google Colab for ML (October 2022)

Google introduced computing units, which you can purchase just like any other cloud computing unit you can from AWS or Azure etc. With Pro you get 100, and with Pro+ you get 500 computing units. GPU, TPU and option of High-RAM effects how much computing unit you use hourly. If you don’t have any computing units, you can’t use “Premium” tier gpus (A100, V100) and even P100 is non-viable.

If you are looking for an all-in-one solution to help you prepare for the AWS Cloud Practitioner Certification Exam, look no further than this AWS Cloud Practitioner CCP CLF-C02 book

Google Colab Pro+ comes with Premium tier GPU option, meanwhile in Pro if you have computing units you can randomly connect to P100 or T4. After you use all of your computing units, you can buy more or you can use T4 GPU for the half or most of the time (there can be a lot of times in the day that you can’t even use a T4 or any kinds of GPU). In free tier, offered gpus are most of the time K80 and P4, which performs similar to a 750ti (entry level gpu from 2014) with more VRAM.

For your consideration, T4 uses around 2, and A100 uses around 15 computing units hourly.
Based on the current knowledge, computing units costs for GPUs tend to fluctuate based on some unknown factor.

Considering those:

  1. For hobbyists and (under)graduate school duties, it will be better to use your own gpu if you have something with more than 4 gigs of VRAM and better than 750ti, or atleast purchase google pro to reach T4 even if you have no computing units remaining.
  2. For small research companies, and non-trivial research at universities, and probably for most of the people Colab now probably is not a good option.
  3. Colab Pro+ can be considered if you want Pro but you don’t sit in front of your computer, since it disconnects after 90 minutes of inactivity in your computer. But this can be overcomed with some scripts to some extend. So for most of the time Colab Pro+ is not a good option.

If you have anything more to say, please let me know so I can edit this post with them. Thanks!

Conclusion:


In machine learning, precision and recall trade off against each other; increasing one often decreases the other. There is no single silver bullet solution for increasing either precision or recall; it depends on your specific use case which one is more important and which methods will work best for boosting whichever metric you choose. In this blog post, we explored some methods for increasing either precision or recall; hopefully this gives you a starting point for improving your own models!

 

What are some ways we can use machine learning and artificial intelligence for algorithmic trading in the stock market?

Machine Learning and Data Science Breaking News 2022 – 2023

  • [D] ML paper verb tense
    by /u/cosmoquester (Machine Learning) on May 26, 2024 at 7:23 am

    Why do most ML papers use all verb tenses in the present tense like MLA format while using a quote or reference section as APA style? In particular, even though academic societies such as ICML explicitly say that they follow the APA style, most of the papers' verb tenses do not seem to be followed by instructions in the APA guide to write the past, present, and future appropriately. submitted by /u/cosmoquester [link] [comments]

  • [Discussion] Prediction Models for multi-tenant system in finance sector
    by /u/Puzzleheaded-Rest734 (Machine Learning) on May 26, 2024 at 7:20 am

    Let's suppose a finance SaaS platform with 100 million customers wants to introduce a sales prediction service. How would one design a system that... Predicts sales forecast based on historical data of each tenant Will it entail training and building 100 million models ( model per tenant) Any guidance here is appreciated. Also, any blog/reference material to read about such design case studies would be helpful. Thanks submitted by /u/Puzzleheaded-Rest734 [link] [comments]

  • [D] Specific to ViT(visual transformers) are there any learnable params in patch embeddings?
    by /u/elongatedpepe (Machine Learning) on May 26, 2024 at 7:13 am

    I'm trying to understand where exactly in ViT are the learnable parameters. First step is to convert patch to patch embeddings to feed into n/w so we add a simple linear transformation (FCN) for dims reduction and matrix->vector. What is learnt here? Are there any weights? Or is it just shrinking the 2d patch input to a 1d vector. Since these patches are processed(linear transformed) in parallel, they have no idea on other patch information. People say patch to patch interaction happen in attention layer BUT there are no learnable params in attention layer it's just transpose and multiple query key patch. Does the backprop in attention layer loss cause weights to change in patch embedding layer?? Also, why do they call as linear transformation of patch embedding? Aren't they adding any activation function, it's supposed to be non linear transformation right? submitted by /u/elongatedpepe [link] [comments]

  • [R] Testing theory of mind in large language models and humans
    by /u/AhmedMostafa16 (Machine Learning) on May 26, 2024 at 6:44 am

    submitted by /u/AhmedMostafa16 [link] [comments]

  • [R] The carbon emissions of writing and illustrating are lower for AI than for humans
    by /u/NavinF (Machine Learning) on May 26, 2024 at 4:53 am

    submitted by /u/NavinF [link] [comments]

  • [R] [CVPR 2024] AV-RIR: Audio-Visual Room Impulse Response Estimation
    by /u/Snoo63916 (Machine Learning) on May 26, 2024 at 4:26 am

    submitted by /u/Snoo63916 [link] [comments]

  • [D] Can Image to Image Diffusion bridges models be used to solve Image segmentation problems?
    by /u/Far-Theory-7027 (Machine Learning) on May 25, 2024 at 9:15 pm

    Can Image to Image Diffusion bridges like BBDM:Image-to-image Translation with Brownian Bridge Diffusion Models be used to solve Image segmentation problems? submitted by /u/Far-Theory-7027 [link] [comments]

  • [D] What's the best way for me to go about building a robust yet human-like playable Poker AI Model
    by /u/HandfulOfAStupidKid (Machine Learning) on May 25, 2024 at 7:50 pm

    I'm working on a (Texas hold 'em) Poker game and I'd like to have an AI that can play at a human-ish level. I've developed a win probability calculator which can find the odds of you having the best hand in the game given your cards, the community cards, and the number of players in the game. I'm unsure of where to go from here. I study ML/AI in school but I've been having a hard time making the best decision on how to actually apply these tools in practice. Firstly, I'm unsure of what dataset to use, I found a dataset of online poker game logs which might useful. Also, I don't know whether to develop a decision tree, use neural networks, or a combination of the two and/or other methods. What's the best way to go about building my AI model using ML for this project? submitted by /u/HandfulOfAStupidKid [link] [comments]

  • [D] Best Clustering Mechanism to fit in the range of minimum and maximum
    by /u/AnywhereOk9403 (Machine Learning) on May 25, 2024 at 6:23 pm

    I have a few pickup points which are lat,long values. Each lat-long value has variable number of items to be picked up. I want to organize them into clusters such that the total number of items in a cluster is in the range of m to n. Which is the best clustering algorithm for my problem? submitted by /u/AnywhereOk9403 [link] [comments]

  • [Research][D] Research scientist manager at big tech
    by /u/No-Trifle2470 (Machine Learning) on May 25, 2024 at 6:00 pm

    Hi all, I am a research scientist manager at a non-tech company. I am managing a team of RS and ML. Our approach is quite different as our goal is not to improve or create a new ml models like fundamental research. Depending on our company long term objectives, we investigate state of the art research papers, investigate to see if it will work on our context and then modify/create if needed. It’s like an applied research. Now, I am interviewing for a research scientist manager at a big tech company and I am wondering how they manage their research team. Think like (Meta GenAI or FAIR, Google brains, …) Thanks for your help. submitted by /u/No-Trifle2470 [link] [comments]

  • [D] Any opinions on Axler's new 2024 Linear Algebra done right book?
    by /u/leao_26 (Machine Learning) on May 25, 2024 at 5:35 pm

    https://www.quora.com/qemail/tc?al_imp=eyJ0eXBlIjogMzMsICJoYXNoIjogIjEzMDQwMjEwMjc2MzA4NTUzNjd8MXwxfDE0Nzc3NDM3NDQ4ODg0OTAifQ%3D%3D&al_pri=1&aoid=4j6K2MFavpW&aoty=2&aty=4&cp=1&et=2&id=fc99edff3f7249998f1385c28372ffed&q_aid=qSwiBLtQJuH&uid=nAH258ceN4 submitted by /u/leao_26 [link] [comments]

  • [D] Showcase ML Model Results Using Business Metrics
    by /u/BeneficialAd3800 (Machine Learning) on May 25, 2024 at 4:46 pm

    submitted by /u/BeneficialAd3800 [link] [comments]

  • [D] Memory tuning vs fine tuning
    by /u/hamsterhooey (Machine Learning) on May 25, 2024 at 1:24 pm

    I came across a LinkedIn post where this startup claims to reduce LLM hallucinations using “memory tuning”. They claim that memory tuning is significantly superior to fine tuning. Was wondering if anyone has come across the term “memory tuning” and whether you think it is marketing talk or a real development. This is the post for reference: https://www.linkedin.com/posts/zhousharon_hallucinations-are-one-of-the-biggest-blockers-activity-7198340103600054273-ruNL?utm_source=share&utm_medium=member_ios submitted by /u/hamsterhooey [link] [comments]

  • [D] Geospatial Trend Analysis Recommendations
    by /u/HRs_Worst_Enemy (Machine Learning) on May 25, 2024 at 11:38 am

    Geospatial Trend Analysis Question What is your guess… I have built a large number of market prediction (domain specific) machine learning models over the past year. My current project involves utilizing geospatial data and transfer learning to analyze Google searcg trends for different stocks. The way I am able to train on limited data is through the use of a synthetic data augmentation process I designed to capture time-scale invariance. One piece of advice if you try to recreate this approach is to use batch training to avoid overfitting especially while transfer learning. (If any of that sounds confusing or dumb please just ignore it because specifics are not necessarily that important.) Currently, I am analyzing data from major cities in the US (including D.C.) and large cities internationally such as Moscow and Beijing. I am curious which city people would assume creates the most accurate model. Which city’s search data is best for predicting future share prices? There are no wrong answers, but I am looking for more cities to add into my model while trying to be cognizant of the size because all of this is done on a ~5 year old laptop. I will note that I am using a pseudo SQL library to manage a database and be (semi) respectful when doing large data pulls. submitted by /u/HRs_Worst_Enemy [link] [comments]

  • [R] Vanilla Clip for 3D
    by /u/Standing_Appa8 (Machine Learning) on May 25, 2024 at 11:37 am

    Hello! I am wondering if there is a CLIP approach https://openai.com/index/clip/ for 3D data? I just found approaches that did "something" like CLIP but not exactly. Can someone point me to a paper or direction? I would especially need this for MRIs. Is someone aware of this? Thank you very much. Maybe my search was not extensive enough but I also couldn't spot a good framework from the papers that I found. submitted by /u/Standing_Appa8 [link] [comments]

  • [R] Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum
    by /u/StartledWatermelon (Machine Learning) on May 25, 2024 at 10:34 am

    TL;DR: do NOT stuff more than one document in the context window while training an LM. Paper: https://arxiv.org/abs/2405.13226 Abstract: Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents of various lengths and then chunking them into sequences of a predetermined target length. However, this method of concatenation can lead to cross-document attention within a sequence, which is neither a desirable learning signal nor computationally efficient. Additionally, training on long sequences becomes computationally prohibitive due to the quadratic cost of attention. In this study, we introduce dataset decomposition, a novel variable sequence length training technique, to tackle these challenges. We decompose a dataset into a union of buckets, each containing sequences of the same size extracted from a unique document. During training, we use variable sequence length and batch size, sampling simultaneously from all buckets with a curriculum. In contrast to the concat-and-chunk baseline, which incurs a fixed attention cost at every step of training, our proposed method incurs a penalty proportional to the actual document lengths at each step, resulting in significant savings in training time. We train an 8k context-length 1B model at the same cost as a 2k context-length model trained with the baseline approach. Experiments on a web-scale corpus demonstrate that our approach significantly enhances performance on standard language evaluations and long-context benchmarks, reaching target accuracy 3x faster compared to the baseline. Our method not only enables efficient pretraining on long sequences but also scales effectively with dataset size. Lastly, we shed light on a critical yet less studied aspect of training large language models: the distribution and curriculum of sequence lengths, which results in a non-negligible difference in performance. Visual Summary: https://preview.redd.it/nnvi519tvj2d1.png?width=1123&format=png&auto=webp&s=334b8990f4ac2d4298e1a622d71301cd7d6beae3 submitted by /u/StartledWatermelon [link] [comments]

  • [R] LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
    by /u/ghoof (Machine Learning) on May 25, 2024 at 10:29 am

    https://huggingface.co/papers/2405.14477 submitted by /u/ghoof [link] [comments]

  • [R] YOLOv10: Real-Time End-to-End Object Detection
    by /u/StartledWatermelon (Machine Learning) on May 25, 2024 at 9:48 am

    Paper: https://arxiv.org/abs/2405.14458 Abstract: Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8× faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8× smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance. Visual Summary: Method Benchmarking Code: https://github.com/THU-MIG/yolov10 submitted by /u/StartledWatermelon [link] [comments]

  • [D] Learning path to ML compilers
    by /u/SPark9625 (Machine Learning) on May 25, 2024 at 9:47 am

    Hi all, I recently got interested in ML compilers, and wanted to ask some questions. 1. ML Compilers vs language compilers I'm curious if ML compilers and language compilers have a lot in common. I only know a bit about the frontend part of a compiler, but it looks like ML compilers don't really have the usual lexical / syntax / semantic analysis phases that language compilers go through. Would the backend part be much more relevant for ML compilers though? Would you recommend learning topics like IR optimization, register/memory allocation or SSA first before getting into ML compilers? It feels like ML compiler is a beast of its own, so I'm not sure if I should just dive into it, or having a background in traditional compiler backend would be still very helpful. At least the MLIR paper seems to talk about SSA and IRs, so maybe having some compiler backend background is necessary? Just to clarify, I know the definition of SSA and IR, but here I'm talking about going in depths about these topics. 2. Focus areas within ML compilers I'm also curious what are the areas that require the most amount of work within ML compilers? Is it the IR (or graph) optimization? Or something else? Do you think this domain is something that will last for decades, or mostly a few years effort and then users won't have to care about the internals anymore (just like how a normal user don't really care about how gcc or clang compiles C code too much nowadays) submitted by /u/SPark9625 [link] [comments]

  • How reputable is SuperAl conference, Singapore? Is it just a money grab, which I hear about a lot of Al conferences recently. There are some good names in it, but not sure about anything else. Also any tips for first time conference goer would be highly appreciated. [D]
    by /u/True-Quarter4596 (Machine Learning) on May 25, 2024 at 7:24 am

    I hope this isn't off topic and the mods would allow it. superai.com submitted by /u/True-Quarter4596 [link] [comments]

Top 100 Data Science and Data Analytics and Data Engineering Interview Questions and Answers

What are some good datasets for Data Science and Machine Learning?

Ace the 2023 AWS Solutions Architect Associate SAA-C03 Exam with Confidence Pass the 2023 AWS Certified Machine Learning Specialty MLS-C01 Exam with Flying Colors

List of Freely available programming books - What is the single most influential book every Programmers should read



#BlackOwned #BlackEntrepreneurs #BlackBuniness #AWSCertified #AWSCloudPractitioner #AWSCertification #AWSCLFC02 #CloudComputing #AWSStudyGuide #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AWSBasics #AWSCertified #AWSMachineLearning #AWSCertification #AWSSpecialty #MachineLearning #AWSStudyGuide #CloudComputing #DataScience #AWSCertified #AWSSolutionsArchitect #AWSArchitectAssociate #AWSCertification #AWSStudyGuide #CloudComputing #AWSArchitecture #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AzureFundamentals #AZ900 #MicrosoftAzure #ITCertification #CertificationPrep #StudyMaterials #TechLearning #MicrosoftCertified #AzureCertification #TechBooks

Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic

Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.

Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.

Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.

Turn your dream into reality with Google Workspace: It’s free for the first 14 days.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes:
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6 96DRHDRA9J7GTN6
63F733CLLY7R7MM
63F7D7CPD9XXUVT
63FLKQHWV3AEEE6
63JGLWWK36CP7WM
63KKR9EULQRR7VE
63KNY4N7VHCUA9R
63LDXXFYU6VXDG9
63MGNRCKXURAYWC
63NGNDVVXJP4N99
63P4G3ELRPADKQU
With Google Workspace, Get custom email @yourcompany, Work from anywhere; Easily scale up or down
Google gives you the tools you need to run your business like a pro. Set up custom email, share files securely online, video chat from any device, and more.
Google Workspace provides a platform, a common ground, for all our internal teams and operations to collaboratively support our primary business goal, which is to deliver quality information to our readers quickly.
Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE
C37HCAQRVR7JTFK
C3AE76E7WATCTL9
C3C3RGUF9VW6LXE
C3D9LD4L736CALC
C3EQXV674DQ6PXP
C3G9M3JEHXM3XC7
C3GGR3H4TRHUD7L
C3LVUVC3LHKUEQK
C3PVGM4CHHPMWLE
C3QHQ763LWGTW4C
Even if you’re small, you want people to see you as a professional business. If you’re still growing, you need the building blocks to get you where you want to be. I’ve learned so much about business through Google Workspace—I can’t imagine working without it.
(Email us for more codes)

error: Content is protected !!