What are some ways to increase precision or recall 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?

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:

  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!

 

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  • [P] New registry for KitOps, an open source MLOps tool: Check out the preview (not gated)
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  • [N] ICML 2024 liveblog
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  • [D] Zero-Shot Entity Matching
    by /u/cedar_mountain_sea28 (Machine Learning) on July 24, 2024 at 9:55 am

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  • [R] Zero Shot LLM Classification
    by /u/SkeeringReal (Machine Learning) on July 24, 2024 at 9:50 am

    I'm surprised there is not more research in zero shot classification with GenAI LLMs? They are pretty darn good at this, and I imagine they will just keep getting better. E.g. see this and this Am I missing anything? As AI advances the next 5 years, it seems inevitable to me that these foundation models will continue to grow in common sense reasoning and be the best out of the box classifiers you can get, and likely start to outperform more task specific models which fail on novel classes or edge cases. Why isn't there more research in this? Do people just feel it's obvious? submitted by /u/SkeeringReal [link] [comments]

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    by /u/alexsht1 (Machine Learning) on July 24, 2024 at 9:40 am

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  • Drinking From a Fire Hose (First 100 Days & Beyond Question)
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    Hey guys, I'm constantly checking for jobs in the sports and gaming analytics industry. I've posted recently in this community and had some good comments. I run www.sportsjobs.online, a job board in that niche. In the last month I added around 200 jobs: I'm celebrating I automated all the NFL teams with this post and doing so I've found a few interesting data science and analytics jobs. Sr. Manager, Data Science @ Houston Texans Data Engineer @ Jacksonville Jaguars CRM Analyst, Business Solutions @ Arizona Cardinals Full Stack Software Engineer @ Tennessee Titans NFL Data Scientist @ Swish Analytics F1 Software Engineer - Vehicle Dynamics @ Williams Racings Software Engineer @ Haas F1 Senior Data & Strategy Analyst @ McLaren There are multiple more jobs related to data science, engineering and analytics in the job board. I've created also a reddit community where I post recurrently the openings if that's easier to check for you. I hope this helps someone! submitted by /u/fark13 [link] [comments]

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  • [D] Ideas for Gamifying an LLM Workshop for New Graduates
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    Hey everyone, I'm looking for ideas to gamify a workshop on LLMs for new college graduates joining our company. The plan is to have hands-on lab exercises in the first half of the day, followed by a hackathon or game in the second half. We have about 6 hours of work time in total. Some background: I previously ran a 3-hour workshop for these students during their internship, covering the basics of gen AI, LLMs, and building a simple RAG-based chatbot for PDFs. Due to system restrictions on work laptops, not everyone could complete the hands-on part. Now they're joining full-time, and this workshop is part of their 3-month training program. I'm trying to improve on the previous session. For context, last year AWS ran a DeepRacer workshop that ended with a race to apply the learnings. I'm aiming for something similar with LLMs – perhaps a game or activity that could serve as a proxy for an LLM, allowing them to apply concepts like RAG and function calling in a competitive setting. The challenge is that the participants have varying levels of programming skills. I'm looking for something that's approachable for beginners but also substantial enough for everyone to learn something beyond basic prompt engineering. If you have any ideas or need more information, let me know. I'm open to suggestions on how to make this an effective learning experience for the new graduates. submitted by /u/nerdimite [link] [comments]

  • [D] Error importing torch_tensorrt
    by /u/Top-Establishment545 (Machine Learning) on July 23, 2024 at 2:03 pm

    hi everyone I am trying to use torch_tensorrt for the first time. I tried installing it with pip install torch-tensorrt and it works fine, but then, when I try to import it in my code, I get an error message: OSError: libtorchtrt.so: undefined symbol: _ZNK3c105Error4whatEv. Does anyone know how to fix this? I could not find anything on the internet. UPDATE: downgrading my python to version 3.10 somehow solved the problem submitted by /u/Top-Establishment545 [link] [comments]

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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
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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.
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