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

Posted on

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

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

Master AI Machine Learning PRO
Elevate Your Career with AI & Machine Learning For Dummies PRO
Ready to accelerate your career in the fast-growing fields of AI and machine learning? Our app offers user-friendly tutorials and interactive exercises designed to boost your skills and make you stand out to employers. Whether you're aiming for a promotion or searching for a better job, AI & Machine Learning For Dummies PRO is your gateway to success. Start mastering the technologies shaping the future—download now and take the next step in your professional journey!

Download on the App Store

Download the AI & Machine Learning For Dummies PRO App:
iOS - Android
Our AI and Machine Learning For Dummies PRO App can help you Ace the following AI and Machine Learning certifications:

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).

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).

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.

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:

Ace the 2024 AWS Certified Machine Learning Specialty Exam with Flying Colors

AI- Powered Jobs Interview Warmup For Job Seekers

  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 good datasets for Data Science and Machine Learning?
Machine Learning Engineer Interview Questions and Answers

 

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] Self-Promotion Thread
    by /u/AutoModerator (Machine Learning) on December 8, 2024 at 3:15 am

    Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. Any abuse of trust will lead to bans. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [comments]

  • How do you manage resources or optimize cost when training models in cloud services like aws sagemaker, or gcp vertex ai? [D]
    by /u/InformationEmpty1440 (Machine Learning) on December 7, 2024 at 10:17 pm

    Hey all, I've been using sagemaker quite a bit lately for training ML models and doing deployments. I know enough about aws and instance types to create training nodes that have enough capacity to train my models, but many times I am underutilizing RAM, GPU memory, or CPUs, so it feels like this leads to a lot of waste (and extra cost). How do you guys figure out what type of instance or resources would best fit your needs without being too wasteful? Is there any way to adjust resources automatically, or any library that could handle that for you? submitted by /u/InformationEmpty1440 [link] [comments]

  • [P] Extract Transcripts with Positive Emotions in batch
    by /u/dmpetrov (Machine Learning) on December 7, 2024 at 9:08 pm

    Check out this example project on how to find transcripts of audio recordings with positive emotions. A good example of a project demonstrating of extract actionable insights from audio! It takes common voice dataset of audio files from hagging face, applies emotion recognition model and whisper-tiny model for the transcripts. All is organized in a nice looking batch pipeline. An interesting detail - No need to extract archives! This pipeline analyzes audio files directly from tar archives, saving you extra steps. Video: https://www.youtube.com/watch?v=OCm5W0L5BTU Colab notebook: https://colab.research.google.com/github/iterative/datachain-examples/blob/main/audio/hf_common_voice.ipynb Jupyter Notebook: https://github.com/iterative/datachain-examples/blob/main/audio/hf_common_voice.ipynb submitted by /u/dmpetrov [link] [comments]

  • [P] I cannot find this open-source transformer on GitHub, released recently, for the life of me.
    by /u/Breck_Emert (Machine Learning) on December 7, 2024 at 7:05 pm

    There was a paper released along with a GitHub repository of an extremely well-made transformer designed for testing out new components. But I can't find it! It's not one of the ones that has existed like HuggingFace ones. Any clue? submitted by /u/Breck_Emert [link] [comments]

  • [R] GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
    by /u/Noname_emanon_ (Machine Learning) on December 7, 2024 at 6:43 pm

    Hi everyone, I’m currently working through the recent paper “GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?”, but I’ve run into some issues and was hoping someone here might have insights. Posterior Distribution: In the implementation, a posterior distribution is used, but I couldn’t find the formula or explanation in the paper. Does anyone know where this comes from or how it’s derived? Asynchronous Model: The paper and its implementation don’t seem entirely consistent when it comes to the asynchronous model. Specifically: Is the generation process done step-by-step asynchronously? Or does it first denoise the attribute vectors entirely before moving on to edge denoising? I’ve tried searching online, but since this is a new paper, there isn’t much discussion or documentation yet. Any help, advice, or pointers would be greatly appreciated! submitted by /u/Noname_emanon_ [link] [comments]

  • Is the data job market as badly affected as software engineering?
    by /u/Will_Tomos_Edwards (Data Science) on December 7, 2024 at 5:38 pm

    Everyone knows the market is bad right now for software engineers. Probably as bad as it's every been. What is the consensus on the job market for data professionals right now? submitted by /u/Will_Tomos_Edwards [link] [comments]

  • How to solve the STT Cutoff Problem [D]
    by /u/Leo2000Immortal (Machine Learning) on December 7, 2024 at 12:04 pm

    Hello folks, I've been working on an agentic solution where you can have an autonomous agent taking live calls. We're using a pipeline of Speech to Text, LLM for generating responses and then Text to Speech. In this pipeline, Speech to text is causing some issues because it's difficult to determine when exactly a sentence is over since the user can take pauses. Moreover, when multiple inputs go into LLM, multiple responses are generated and they queue up for Text to speech. How would you solve this problem? How would you also handle cases where the user interrupts the agent? submitted by /u/Leo2000Immortal [link] [comments]

  • [D] How to actually prevent overfitting in practice in ScikitLearn ?
    by /u/desslyie (Machine Learning) on December 7, 2024 at 9:55 am

    We all saw in class the trade off between bias and variance, that we don't want our train loss to keep going down and our test loss go up. But in practice I feel like doing hyperparameter tuning for classic ML models with GridSearchCV / BayesSearchCV is not enough. Even though I do cross validation, the search.best_model obtained at the end is almost always overfitting. How can you actually perform a search that will give you a robust generalized model with higher chances ? submitted by /u/desslyie [link] [comments]

  • [N] Sama, an AI sweatshop, pays workers in Kenya $2 an hour to filter and label porn, beastiality, suicide, child abuse, for hours on end!!
    by /u/BotherBubbly5096 (Machine Learning) on December 7, 2024 at 7:38 am

    submitted by /u/BotherBubbly5096 [link] [comments]

  • [R] Zero shot Meme-interpretability of LLMs
    by /u/No_Cartoonist8629 (Machine Learning) on December 7, 2024 at 7:27 am

    Head to head of meme-interpretability with the same image and text prompt! Anecdotal but interesting responses. Also clear winner! submitted by /u/No_Cartoonist8629 [link] [comments]

  • [R] For a change of topic: some nonLLM focused work of mine: Bias-Free Sentiment Analysis through Semantic Blinding and Graph Neural Networks
    by /u/Hub_Pli (Machine Learning) on December 7, 2024 at 6:21 am

    In my academic field (social sciences) I deal with the problem of bias in SA models. My previous work showed that deep learning SA systems inherit bias (e.g. nonrepresentative of the population political bias) from annotators: https://arxiv.org/abs/2407.13891 Now I devised a solution that used a technique I call semantic blinding to provide only the bare necessary information for the model to predict emotions in text, leaving no signal for the model to overfit and produce bias from: https://arxiv.org/abs/2411.12493 Interested to hear your thoughts before I publish the SProp Gnn. Do you think it could be useful beyond the academia? submitted by /u/Hub_Pli [link] [comments]

  • [D] AAAI 2025 Phase 2 Decision
    by /u/No-Style-7975 (Machine Learning) on December 7, 2024 at 4:27 am

    When would the phase 2 decision come out? I know the date is December 9th, but would there be chances for the result to come out earlier than the announced date? or did it open the result at exact time in previous years? (i.e., 2024, 2023, 2022 ....) Kinda make me sick to keep waiting. submitted by /u/No-Style-7975 [link] [comments]

  • Llama3.3 free API
    by /u/mehul_gupta1997 (Data Science) on December 7, 2024 at 3:09 am

    submitted by /u/mehul_gupta1997 [link] [comments]

  • [R] JAX vs TensorFlow-XLA
    by /u/Odd-Detective289 (Machine Learning) on December 7, 2024 at 3:02 am

    Few months ago, I migrated from TF 2.0 to Jax. I found that jax is significantly faster than Tf. I noticed in the official documentation that it relies on XLA default that uses JIT compilation which makes execution faster. I also noticed that TF graphs also have option to enable JIT compilation with XLA. But still jax dominates TF with XLA. I just want to know why. submitted by /u/Odd-Detective289 [link] [comments]

  • [D] Multimodal AI
    by /u/Frosty_Programmer672 (Machine Learning) on December 6, 2024 at 11:17 pm

    Multimodal AI is changing the game by combining text, images, and even video into a single, cohesive system. It’s being talked about as a major leap in AI capabilities. What industries do you think will benefit the most from this tech? And are there any challenges you see in integrating these models into everyday use? Would love to hear everyone's thoughts! submitted by /u/Frosty_Programmer672 [link] [comments]

  • Classification threshold cost optimisation
    by /u/hazzaphill (Data Science) on December 6, 2024 at 10:29 pm

    Say you’ve selected the best classifier for a particular problem, using threshold invariant metrics such as AUROC, Brier score, or log loss. It’s now time to choose the classification threshold. This will clearly depend on the use case and the cost/ benefits associated with true positives, false positives, etc. Often I see people advising to choose a threshold by looking at metrics such precision and recall. What I don’t see very often is people explicitly defining relative (or absolute, if possible) costs/ benefits of each cell in the confusion matrix (or more precisely the action that will be taken as a result). For example a true positive is worth $1000, a false positive -$500 and the other cells $0. You then optimise the threshold based on maximum benefit using a cost-threshold curve. The precision and recall can also be reported, but they are secondary to the benefit optimisation and not used directly in the choice. I find this much more intuitive and is my go-to. Does anyone else regularly use this approach? In what situations might this approach not make sense? submitted by /u/hazzaphill [link] [comments]

  • [D] selective transfer learning
    by /u/reshail_raza (Machine Learning) on December 6, 2024 at 9:30 pm

    Hello everyone, I am looking for methods that can automatically categorize and select layers from for transfer learning. If you know any such methods or research please let me know or share. Thanks submitted by /u/reshail_raza [link] [comments]

  • [R] Agentic Retrieval Augmented Generation with Memory
    by /u/External_Ad_11 (Machine Learning) on December 6, 2024 at 7:10 pm

    Imagine a customer support chatbot for an e-commerce platform that retrieves relevant product details from its knowledge base and performs web searches for additional information. Furthermore, it remembers past conversations to deliver a seamless and personalized experience for returning users. Here is how it works: - Store your own data in the knowledge base—in our case, a Website URL. - Convert the data into embeddings and save it in the Qdrant Vector Database. - Use phidata Agentic Workflow to combine Tools, LLM, Memory, and the Knowledge Base. Code Implementation Video: https://www.youtube.com/watch?v=CDC3GOuJyZ0 submitted by /u/External_Ad_11 [link] [comments]

  • Meta released Llama3.3
    by /u/mehul_gupta1997 (Data Science) on December 6, 2024 at 5:53 pm

    submitted by /u/mehul_gupta1997 [link] [comments]

  • [R] Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis
    by /u/_puhsu (Machine Learning) on December 6, 2024 at 4:58 pm

    New paper and code for the scale-wise transformer for fast text-to-image generation from our team at Yandex Research Switti outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being faster than distilled diffusion models. Code with checkpoints: https://github.com/yandex-research/switti Generation examples submitted by /u/_puhsu [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)