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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.

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).

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).

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).

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.
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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:
- 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.
- 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.
- 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!
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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!
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Machine Learning and Data Science Breaking News 2022 – 2023
- FCC Text data?by /u/ib33 (Data Science) on February 14, 2025 at 12:26 am
I'm looking to do some project(s) regarding telecommunications. Would I have to build an "FCC_publications" dataset from scratch? I'm not finding one on their site or others. Also, what's the standard these days for storing/sharing a dataset like that? I can't imagine it's CSV. But is it just a zip file with folders/documents inside? submitted by /u/ib33 [link] [comments]
- [Discussion] How AI exactly have their own consciousness?by /u/Illustrious_Board_75 (Machine Learning) on February 13, 2025 at 10:12 pm
Hey, I’m sure this question has been asked before, but I’m an IT support guy who recently got interested in AI. I understand the basics of neural networks—how they take inputs, apply weights, sum them up, add bias, and pass through an activation function. At the end of the day, it’s just optimizing a huge mathematical function. But when scaled up, AI models seem to develop their “own way of thinking” or even “sense of self.” How does that emerge purely from mathematical optimization? Is it just an illusion of complexity, or is there something deeper going on? Would love to hear thoughts from those more experienced in AI! submitted by /u/Illustrious_Board_75 [link] [comments]
- [R] Mutation-Guided LLM-based Test Generation at Metaby /u/AhmedMostafa16 (Machine Learning) on February 13, 2025 at 9:26 pm
submitted by /u/AhmedMostafa16 [link] [comments]
- [D] How did you find your specialty?by /u/violincasev2 (Machine Learning) on February 13, 2025 at 8:39 pm
For context, I’m an undergrad looking forward to applying to PhD programs next year. I’m certain I want to study ML, but that’s a very broad topic. I’ve dipped my toes all around, doing research/projects in NLP, interpretability, diffusion, recommendation systems, manifold/geometric methods, and will be doing work in music and maybe in RL. How did you all find your domains, and how important is it to know precisely what I want going into grad school? submitted by /u/violincasev2 [link] [comments]
- What companies/industries are “slow-paced”/low stress?by /u/_hairyberry_ (Data Science) on February 13, 2025 at 8:02 pm
I’ve only ever worked in data science for consulting companies, which are inherently fast-paced and quite stressful. The money is good but I don’t see myself in this field forever. “Fast-pace” in my experience can be a code word for “burn you out”. Out of curiosity, do any of you have lower stress jobs in data science? My guess would be large retailers/corporations that are no longer in growth stage and just want to fine tune/maintain their production models, while also dedicating some money to R&D with more reasonable timelines submitted by /u/_hairyberry_ [link] [comments]
- Advice on what I should refresh my knowledge on for an interview."by /u/Amazing_Alarm6130 (Data Science) on February 13, 2025 at 7:56 pm
I have an interview in six days. What should I prioritize in my studies based on what the recruiter shared with me (see below) ? Recruiter email: "Technical Screen: Deep Learning. This technical interview will assess your understanding of deep learning fundamentals and your ability to apply these concepts to scientific discovery. The discussion will focus on core theoretical principles, algorithmic intuition, and practical implementations relevant to scientific research." submitted by /u/Amazing_Alarm6130 [link] [comments]
- Mcafee data scientistby /u/lostmillenial97531 (Data Science) on February 13, 2025 at 7:25 pm
Anyone has gone through Mcafee data science coding assessment? Looking for some insights on the assessment. submitted by /u/lostmillenial97531 [link] [comments]
- [D] Upscaling modelby /u/jiraiya1729 (Machine Learning) on February 13, 2025 at 6:56 pm
I need a model which upscales the current image resolution with more emphasis on inference time ( in milli secs ) Do you guys know any model? submitted by /u/jiraiya1729 [link] [comments]
- Data Science internship: New York Times vs CVS Healthby /u/victorian_secrets (Data Science) on February 13, 2025 at 5:24 pm
NLP focused PhD student looking to pivot to industry choosing between two offers. CVS: likely focused on health insurance data science; much more classical A/B testing, experimental design, business metrics, statistics etc. Team matching is still in a long time, so won't know exactly what project I will work on. $55 per hour in NYC with $3000 relocation NYT: ads data science, some kind of graph recommendation system project. Seems more machine learning/neural networks heavy. Interviewed directly with the manager, he seems smart with more expertise in NLP. Project will also involve more text data/social science stuff which is closer to my research. Only $40 per hour and probably no relocation. submitted by /u/victorian_secrets [link] [comments]
- Quick pipeline demos with LLMsby /u/No_Information6299 (Data Science) on February 13, 2025 at 5:18 pm
When you are starting a new project you usually have to collect data, train a model, do evaluations and then present the results to the client. With LLMs, you can quickly create pipelines that allow you to demo/use the functionality of a specialized model without big money or time investment. I have created a collection of classic data science pipelines you can freely use to quickly deliver POC and light pipeline solutions with the use of LLMs. Github repo: Link submitted by /u/No_Information6299 [link] [comments]
- [R] AlignRec Outperforms SOTA Models in Multimodal Recommendationsby /u/skeltzyboiii (Machine Learning) on February 13, 2025 at 5:13 pm
AlignRec, introduced in AlignRec: Aligning and Training in Multimodal Recommendations (CIKM '24), tackles misalignment in multimodal recommendation systems. Traditional methods struggle to integrate diverse content types—text, images, and categorical IDs—due to semantic gaps. AlignRec addresses this by optimizing three alignment tasks: inter-content (ICA), content-category (CCA), and user-item (UIA). ICA unifies semantic representations with an attention-based encoder, CCA enhances feature alignment using contrastive learning, and UIA refines user-item representations via cosine similarity loss. A key innovation is AlignRec’s two-stage training: pre-training aligns visual and textual data, while fine-tuning incorporates user behavior for optimized recommendations. Tested on Amazon datasets, it outperforms nine SOTA models, excelling in long-tail recommendations. By bridging multimodal semantic gaps, AlignRec improves both accuracy and robustness, advancing multimodal AI-driven recommendations. For a deeper dive into the framework and results, see the full paper write-up here: https://www.shaped.ai/blog/multimodal-alignment-for-recommendations submitted by /u/skeltzyboiii [link] [comments]
- [D] How you do ML research from scratch?by /u/AntelopeWilling2928 (Machine Learning) on February 13, 2025 at 4:40 pm
Someone who has published their works at top ML conferences (NIPS, ICML, ICLR) or domain oriented conferences (CVPR, ICCV, ACL, EMNLP, KDD, SIGIR). 1. How do you get from 0 to your first paper? 2. How much is your skill (Pytorch, or domain knowledge)? 3. What is the whole process that you follow to become good at implementing your ideas? 4. How do you come up with an idea and solution? submitted by /u/AntelopeWilling2928 [link] [comments]
- [R] SWE-agent is the new open-source SOTA on SWE-bench Liteby /u/ofirpress (Machine Learning) on February 13, 2025 at 3:35 pm
SWE-agent is an open source software engineering agent that works with any kind of model. Our 1.0 release adds tons of new features: massively parallel runs; cloud-based deployment; extensive configurability with tool bundles; new command line interface & utilities. Completely open-source (MIT), extensive configuration, easy to hack. Since it uses LiteLLM for LM interfacing, you can use it with a local LM: we've used it with Qwen and other community members have used it with Llama. https://github.com/swe-agent/swe-agent SWE-agent is now powered by our new SWE-ReX package (also MIT licensed), a lightweight, general purpose sandboxed code execution engine that supports local Docker, AWS, Modal deployments https://github.com/SWE-agent/swe-rex. You can use it to easily build your own agent with code execution from scratch without the hassle of figuring out how to communicate with running docker containers! SWE-agent is developed by us at Princeton University & Stanford. We'll be here if you have any questions. submitted by /u/ofirpress [link] [comments]
- How do you market yourself when you don’t have model development experience but a ton of experience working “with” models?by /u/Lamp_Shade_Head (Data Science) on February 13, 2025 at 3:28 pm
I work at a large organization where processes are highly structured, and roles are well-defined. Due to a lack of new model development projects, I’ve spent the last three years managing models already in production. My work includes performance monitoring, automating monitoring pipelines, and addressing data and model drift. I have a deep understanding of the models I manage, including their development history and behavior in production. Lately, I’ve been applying for external roles, but most require hands-on model development experience, which I don’t have. This has left me feeling like I’ve wasted the past three years and has made me quite anxious. I know banks value this type of experience, but I’m not interested in working in that sector. So, how can I position my experience to land a new role? submitted by /u/Lamp_Shade_Head [link] [comments]
- Is Managing Unstructured Data a Pain Point for the AI/RAG Ecosystem? Can It Be Solved by Well-Designed Software?by /u/Weird_ftr (Data Science) on February 13, 2025 at 3:23 pm
Hey Redditors, I've been brainstorming about a software solution that could potentially address a significant gap in the AI-enhanced information retrieval systems, particularly in the realm of Retrieval-Augmented Generation (RAG). While these systems have advanced considerably, there's still a major production challenge: managing the real-time validity, updates, and deletion of documents forming the knowledge base. Currently, teams need to appoint managers to oversee the governance of these unstructured data, similar to how structured databases like SQL are managed. This is a complex task that requires dedicated jobs and suitable tools. Here's my idea: develop a unified user interface (UI) specifically for document ingestion, advanced data management, and transformation into synchronized vector databases. The final product would serve as a single access point per document base, allowing clients to perform semantic searches using their AI agents. The UI would encourage data managers to keep their information up-to-date through features like notifications, email alerts, and document expiration dates. The project could start as open-source, with a potential revenue model involving a paid service to deploy AI agents connected to the document base. Some technical challenges include ensuring the accuracy of embeddings and dealing with chunking strategies for document processing. As technology advances, these hurdles might lessen, shifting the focus to the quality and relevance of the source document base. Do you think a well-designed software solution could genuinely add value to this industry? Would love to hear your thoughts, experiences, and any suggestions you might have. Do you know any existing open source software ? Looking forward to your insights! submitted by /u/Weird_ftr [link] [comments]
- [D] Could reasoning LLMs help use identify relevant works a lot better today?by /u/MadEyeXZ (Machine Learning) on February 13, 2025 at 3:05 pm
I know there are lots of helpful services that help you digest the latest papers in arXiv, like arxiv-sanity, paper digest, arXivist, IArxiv, etc. Most of them uses ML (TF-IDF) to rank papers according to your interest, but even with their help, I am still flooded with papers. Most of the tools are built pre-LLM (especially pre-reasoning model), do you guys think reasoning LLMs could help us identify relevant works from arXiv daily publication a lot better? Or have you heard of any existing approaches? submitted by /u/MadEyeXZ [link] [comments]
- [R] Text-to-SQL in Enterprises: Comparing approaches and what worked for usby /u/SirComprehensive7453 (Machine Learning) on February 13, 2025 at 1:44 pm
Hi everyone! Text-to-SQL is a popular GenAI use case, and we recently worked on it with some enterprises. Sharing our learnings here! These enterprises had already tried different approaches—prompting the best LLMs like O1, using RAG with general-purpose LLMs like GPT-4o, and even agent-based methods using AutoGen and Crew. But they hit a ceiling at 85% accuracy, faced response times of over 20 seconds (mainly due to errors from misnamed columns), and dealt with complex engineering that made scaling hard. We found that fine-tuning open-weight LLMs on business-specific query-SQL pairs gave 95% accuracy, reduced response times to under 7 seconds (by eliminating failure recovery), and simplified engineering. These customized LLMs retained domain memory, leading to much better performance. We put together a comparison of all tried approaches on medium. Let me know your thoughts and if you see better ways to approach this. https://preview.redd.it/kqfabsdkuwie1.png?width=1920&format=png&auto=webp&s=88251e0cfa246f2bf1f779e708ab03a96a3c0255 submitted by /u/SirComprehensive7453 [link] [comments]
- [D] License issue with self-collected dataset using online imageby /u/RepresentativeAd985 (Machine Learning) on February 13, 2025 at 1:34 pm
So I am working on a dataset by collecting and annotating online images. Unfortunately not all of the images are under CC license. Is it appropriate to only include links for these images in my published dataset? (Like is it considered fair use or would it causes any trouble?) Is there any popular public image datasets including images not under CC license that I should refer to? I’m very not familiar with these copyright related things so apologies in advance if I made any mistakes in the description of the question. submitted by /u/RepresentativeAd985 [link] [comments]
- Data Team Benchmarksby /u/Different_Eggplant97 (Data Science) on February 13, 2025 at 11:07 am
I put together some charts to help benchmark data teams: http://databenchmarks.com/ For example Average data team size as % of the company (hint: 3%) Median salary across data roles for 500 job postings in Europe Distribution of analytics engineers, data engineers, and analysts The data-to-engineer ratio at top tech companies The data comes from LinkedIn, open job boards, and a few other sources. submitted by /u/Different_Eggplant97 [link] [comments]
- [R] Automated Capability Discovery: Using Foundation Models to Self-Explore and Evaluate AI Abilitiesby /u/Successful-Western27 (Machine Learning) on February 13, 2025 at 9:43 am
This paper introduces a framework called Automated Capability Discovery (ACD) that uses one foundation model to systematically explore and evaluate the capabilities of another model. The core idea is to treat capability discovery as an experimental science, where one model acts as a scientist generating hypotheses and designing tests. Key technical points: - Framework consists of four main components: task generation, execution, evaluation, and analysis - Uses prompting strategies to make the evaluator model generate diverse, meaningful tests - Implements a feedback loop where test results inform future task generation - Evaluation includes both binary success/failure and detailed analysis - Tested on GPT-4, Claude, and Llama models as both evaluators and subjects Results: - Discovered thousands of previously undocumented capabilities - 89% agreement between AI evaluator and human verification on capability assessments - Generated tests covered broad capability categories from basic (arithmetic) to complex (creative writing) - Successfully identified known model limitations - Showed strong correlation between automated and manual evaluation methods I think this approach could transform how we understand and evaluate AI systems. Instead of relying solely on predefined benchmarks or manual testing, we could have continuous, automated exploration of model capabilities. This would be especially valuable for rapid testing of new models and identifying unexpected abilities or limitations. I think the main challenge will be ensuring the evaluator model isn't limited by the same blindspots as the subject model. There's also the question of how well this generalizes beyond language models to other AI architectures. TLDR: New framework uses AI models to automatically discover and evaluate the capabilities of other AI models, showing strong agreement with human evaluations and finding thousands of previously unknown abilities. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]
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Health Health, a science-based community to discuss human health
- Senate votes to confirm Robert F. Kennedy Jr. as health secretaryby /u/Healthy_Block3036 on February 13, 2025 at 4:46 pm
submitted by /u/Healthy_Block3036 [link] [comments]
- Ozempic shown to reduce drinking in first trial in alcohol-use disorderby /u/countdookee on February 13, 2025 at 4:40 pm
submitted by /u/countdookee [link] [comments]
- Senate votes to confirm Robert F. Kennedy Jr. as health secretaryby /u/nbcnews on February 13, 2025 at 4:35 pm
submitted by /u/nbcnews [link] [comments]
- Measles outbreak in Texas was "completely preventable," infectious disease expert saysby /u/CBSnews on February 13, 2025 at 3:42 pm
submitted by /u/CBSnews [link] [comments]
- U.S. investors, Big Pharma race to find new medicines in Chinaby /u/snakkerdudaniel on February 13, 2025 at 2:49 pm
submitted by /u/snakkerdudaniel [link] [comments]
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.
- TIL that Nazi general Erwin Rommel was allowed to take cyanide after being implicated in a plot to kill Hitler. To maintain morale, the Nazis gave him a state funeral and falsely claimed he died from war injuries.by /u/mvincen95 on February 13, 2025 at 3:33 pm
submitted by /u/mvincen95 [link] [comments]
- TIL about Richard Sakakida, an American spy operating in the Philippines before the attack on Pearl Harbor, who spied on the Japanese community in Manila before he was captured after the fall of Corregidor. During his capture, he was tortured and eventually led a jailbreak of about 500 prisoners.by /u/fireatjaps2 on February 13, 2025 at 3:32 pm
submitted by /u/fireatjaps2 [link] [comments]
- TIL the founder of North Face, Douglas Tompkins, was killed in 2015 in a kayaking accident while traveling with long time friend Patagonia founder Yvon Chouinard, in Patagonia, Chile.by /u/mvincen95 on February 13, 2025 at 3:25 pm
submitted by /u/mvincen95 [link] [comments]
- TIL that GameBoy and GameBoy Color cartridges have a watch battery inside of them to power the chip for savefiles.by /u/Dorsai_Erynus on February 13, 2025 at 2:30 pm
submitted by /u/Dorsai_Erynus [link] [comments]
- TIL about the All-American Basketball Alliance, a white-only basketball league proposed in 2010 by boxing promoter Don Lewis. After being decried by mayors and colleges in the cities where teams were proposed, as well as by national media figures, the idea was abandoned.by /u/a3poify on February 13, 2025 at 1:46 pm
submitted by /u/a3poify [link] [comments]
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.
- Stress of Eviction or Housing Loss Linked to Child Mental Health Issues, Study Findsby /u/EffectiveAffect on February 13, 2025 at 3:44 pm
submitted by /u/EffectiveAffect [link] [comments]
- Study suggests sex can provide relationship satisfaction boost that lasts longer than just act itself. Positive “afterglow” of sex can linger for at least 24 hours, especially when sex is a mutual decision or initiated by one partner, while sexual rejection creates negative effect for several days.by /u/mvea on February 13, 2025 at 1:54 pm
submitted by /u/mvea [link] [comments]
- Researchers have successfully grown bioengineered teeth in pigs using a combination of human and pig cells | While the science is still in its early stages, the findings could one day lead to a future where you could have your missing teeth replaced with biological dentition.by /u/chrisdh79 on February 13, 2025 at 1:30 pm
submitted by /u/chrisdh79 [link] [comments]
- Researchers find cancer's 'off-grid' power supply and how to cut it | Researchers have discovered a particular type of cancer cell that relies on its own biological electric utility. Disrupting the utility with the help of a puffer fish showed a breakthrough way to fight the tumors in mice.by /u/chrisdh79 on February 13, 2025 at 12:28 pm
submitted by /u/chrisdh79 [link] [comments]
- Blood test paves the way for better heart attack preventionby /u/uniofreading on February 13, 2025 at 11:19 am
submitted by /u/uniofreading [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- 18 year-old promising Chinese footballer, Guo Jiaxuan, left ‘brain-dead’ after being hit in the head by another player’s knee during a training campby /u/ModenaR on February 13, 2025 at 3:47 pm
submitted by /u/ModenaR [link] [comments]
- Arne Slot: What happens after Liverpool manager was shown a red card?by /u/Fatimamohammadi_ on February 13, 2025 at 2:38 pm
submitted by /u/Fatimamohammadi_ [link] [comments]
- FA Cup to use semi-automated offsides for first timeby /u/renome on February 13, 2025 at 2:17 pm
submitted by /u/renome [link] [comments]
- Up to 3 years in prison for attempt to blackmail Michael Schumacher’s family for $15.6M following convictionsby /u/Oldtimer_2 on February 13, 2025 at 2:06 pm
submitted by /u/Oldtimer_2 [link] [comments]
- TIL that sports analyst, Stephen A. Smith, has a recurring role as “Brick” on the daytime soap opera, General Hospital, playing a surveillance expert for a mob kingpin.by /u/Major-Tuddy on February 13, 2025 at 1:20 pm
submitted by /u/Major-Tuddy [link] [comments]