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?](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_824,h_476/https://enoumen.com/wp-content/uploads/2022/10/image-2.png)
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](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_525,h_835/https://enoumen.com/wp-content/uploads/2020/11/2023_AWS_machine_learning_practice_exams5-644x1024.jpeg)
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?](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_936,h_340/https://enoumen.com/wp-content/uploads/2022/10/image-3.png)
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?](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_1024,h_645/https://enoumen.com/wp-content/uploads/2022/10/image-4-1024x645.png)
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?](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_1024,h_638/https://enoumen.com/wp-content/uploads/2022/10/image-5-1024x638.png)
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)
![](https://www.redditstatic.com/desktop2x/img/renderTimingPixel.png)
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
- [P] Proportionately split dataframe with multiple target columnsby /u/Individual_Ad_1214 (Machine Learning) on July 26, 2024 at 10:24 pm
I have a dataframe with 30 rows and 10 columns. 5 of the columns are input features and the other 5 are output/target columns. The target columns contain classes represented as 0, 1, 2. I want to split the dataset into train and test such that, in the train set, for each output column, the proportion of class 1 is between 0.15 and 0.3. (I am not bothered about the distribution of classes in the test set). ADDITIONAL CONTEXT: I am trying to balance the output classes in a multi-class and multi-output dataset. My understanding is that this would be an optimization problem with 25 (?) degrees of freedom. So if I have any input dataset, I would be able to create a subset of that input dataset which is my training data and which has the desired class balance (i.e class 1 between 0.15 and 0.3 for each output column). I make the dataframe using this import pandas as pd import numpy as np from sklearn.model_selection import train_test_split np.random.seed(42) data = pd.DataFrame({ 'A': np.random.rand(30), 'B': np.random.rand(30), 'C': np.random.rand(30), 'D': np.random.rand(30), 'E': np.random.rand(30), 'F': np.random.choice([0, 1, 2], 30), 'G': np.random.choice([0, 1, 2], 30), 'H': np.random.choice([0, 1, 2], 30), 'I': np.random.choice([0, 1, 2], 30), 'J': np.random.choice([0, 1, 2], 30) }) My current silly/harebrained solution for this problem involves using two separate functions. I have a helper function that checks if the proportions of class 1 in each column is within my desired range def check_proportions(df, cols, min_prop = 0.15, max_prop = 0.3, class_category = 1): for col in cols: prop = (df[col] == class_category).mean() if not (min_prop <= prop <= max_prop): return False return True def proportionately_split_data(data, target_cols, min_prop = 0.15, max_prop = 0.3): while True: random_state = np.random.randint(100_000) train_df, test_df = train_test_split(data, test_size = 0.3, random_state = random_state) if check_proportions(train_df, target_cols, min_prop, max_prop): return train_df, test_df Finally, I run the code using target_cols = ["F", "G", "H", "I", "J"] train, test = proportionately_split_data(data, target_cols) My worry with this current "solution" is that it is probabilistic and not deterministic. I can see the proportionately_split_data getting stuck in an infinite loop if none of the random state I set in train_test_split can randomly generate data with the desired proportion. Any help would be much appreciated! I apologize for not providing this earlier, for a Minimal working example, the input (data) could be A B C D E OUTPUT_1 OUTPUT_2 OUTPUT_3 OUTPUT_4 OUTPUT_5 5.65 3.56 0.94 9.23 6.43 0 1 1 0 1 7.43 3.95 1.24 7.22 2.66 0 0 0 1 2 9.31 2.42 2.91 2.64 6.28 2 1 2 2 0 8.19 5.12 1.32 3.12 8.41 1 2 0 1 2 9.35 1.92 3.12 4.13 3.14 0 1 1 0 1 8.43 9.72 7.23 8.29 9.18 1 0 0 2 2 4.32 2.12 3.84 9.42 8.19 0 0 0 0 0 3.92 3.91 2.90 8.19 8.41 2 2 2 2 1 7.89 1.92 4.12 8.19 7.28 1 1 2 0 2 5.21 2.42 3.10 0.31 1.31 2 0 1 1 0 which has 10 rows and 10 columns, and an expected output (train set) could be A B C D E OUTPUT_1 OUTPUT_2 OUTPUT_3 OUTPUT_4 OUTPUT_5 5.65 3.56 0.94 9.23 6.43 0 1 1 0 1 7.43 3.95 1.24 7.22 2.66 0 0 0 1 2 9.31 2.42 2.91 2.64 6.28 2 1 2 2 0 8.19 5.12 1.32 3.12 8.41 1 2 0 1 2 8.43 9.72 7.23 8.29 9.18 1 0 0 2 2 3.92 3.91 2.90 8.19 8.41 2 2 2 2 1 5.21 2.42 3.10 0.31 1.31 2 0 1 1 0 Whereby each output column in the train set has at least 2 (>= 0.15 * number of rows in input data) instances of Class 1 and at most 3 (<= 0.3 * number of rows in input data). I guess I also didn't clarify that the proportion is in relation to the number of examples (or rows) in the input dataset. My test set would be the remaining rows in the input dataset. submitted by /u/Individual_Ad_1214 [link] [comments]
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- [P] How to make "Out-of-sample" Predictionsby /u/Individual_Ad_1214 (Machine Learning) on July 25, 2024 at 7:47 pm
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List of Freely available programming books - What is the single most influential book every Programmers should read
- Bjarne Stroustrup - The C++ Programming Language
- Brian W. Kernighan, Rob Pike - The Practice of Programming
- Donald Knuth - The Art of Computer Programming
- Ellen Ullman - Close to the Machine
- Ellis Horowitz - Fundamentals of Computer Algorithms
- Eric Raymond - The Art of Unix Programming
- Gerald M. Weinberg - The Psychology of Computer Programming
- James Gosling - The Java Programming Language
- Joel Spolsky - The Best Software Writing I
- Keith Curtis - After the Software Wars
- Richard M. Stallman - Free Software, Free Society
- Richard P. Gabriel - Patterns of Software
- Richard P. Gabriel - Innovation Happens Elsewhere
- Code Complete (2nd edition) by Steve McConnell
- The Pragmatic Programmer
- Structure and Interpretation of Computer Programs
- The C Programming Language by Kernighan and Ritchie
- Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein
- Design Patterns by the Gang of Four
- Refactoring: Improving the Design of Existing Code
- The Mythical Man Month
- The Art of Computer Programming by Donald Knuth
- Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
- Gödel, Escher, Bach by Douglas Hofstadter
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Effective C++
- More Effective C++
- CODE by Charles Petzold
- Programming Pearls by Jon Bentley
- Working Effectively with Legacy Code by Michael C. Feathers
- Peopleware by Demarco and Lister
- Coders at Work by Peter Seibel
- Surely You're Joking, Mr. Feynman!
- Effective Java 2nd edition
- Patterns of Enterprise Application Architecture by Martin Fowler
- The Little Schemer
- The Seasoned Schemer
- Why's (Poignant) Guide to Ruby
- The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
- The Art of Unix Programming
- Test-Driven Development: By Example by Kent Beck
- Practices of an Agile Developer
- Don't Make Me Think
- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
- Domain Driven Designs by Eric Evans
- The Design of Everyday Things by Donald Norman
- Modern C++ Design by Andrei Alexandrescu
- Best Software Writing I by Joel Spolsky
- The Practice of Programming by Kernighan and Pike
- Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
- Software Estimation: Demystifying the Black Art by Steve McConnel
- The Passionate Programmer (My Job Went To India) by Chad Fowler
- Hackers: Heroes of the Computer Revolution
- Algorithms + Data Structures = Programs
- Writing Solid Code
- JavaScript - The Good Parts
- Getting Real by 37 Signals
- Foundations of Programming by Karl Seguin
- Computer Graphics: Principles and Practice in C (2nd Edition)
- Thinking in Java by Bruce Eckel
- The Elements of Computing Systems
- Refactoring to Patterns by Joshua Kerievsky
- Modern Operating Systems by Andrew S. Tanenbaum
- The Annotated Turing
- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
- The Deadline: A Novel About Project Management by Tom DeMarco
- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
- Agile Principles, Patterns, and Practices in C# by Robert C. Martin
- Growing Object-Oriented Software, Guided by Tests
- Framework Design Guidelines by Brad Abrams
- Object Thinking by Dr. David West
- Advanced Programming in the UNIX Environment by W. Richard Stevens
- Hackers and Painters: Big Ideas from the Computer Age
- The Soul of a New Machine by Tracy Kidder
- CLR via C# by Jeffrey Richter
- The Timeless Way of Building by Christopher Alexander
- Design Patterns in C# by Steve Metsker
- Alice in Wonderland by Lewis Carol
- Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
- About Face - The Essentials of Interaction Design
- Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky
- The Tao of Programming
- Computational Beauty of Nature
- Writing Solid Code by Steve Maguire
- Philip and Alex's Guide to Web Publishing
- Object-Oriented Analysis and Design with Applications by Grady Booch
- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
- The Tao Te Ching
- The Productive Programmer
- The Art of Deception by Kevin Mitnick
- The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
- Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
- Masters of Doom
- Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
- How To Solve It by George Polya
- The Alchemist by Paulo Coelho
- Smalltalk-80: The Language and its Implementation
- Writing Secure Code (2nd Edition) by Michael Howard
- Introduction to Functional Programming by Philip Wadler and Richard Bird
- No Bugs! by David Thielen
- Rework by Jason Freid and DHH
- JUnit in Action
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Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
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Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic
- The pull-out method: Why this common contraceptive fails to deliverby /u/Kampala_Dispatch on July 26, 2024 at 7:51 pm
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- Health Canada data reveals surprising number of adverse cannabis reactions (spoiler: it's small)by /u/carajuana_readit on July 26, 2024 at 5:49 pm
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- Online portals deliver scary health news before doctors can weigh inby /u/washingtonpost on July 26, 2024 at 4:37 pm
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- Vaccine 'sharply cuts risk of dementia' new study findsby /u/SubstantialSnow7114 on July 26, 2024 at 1:53 pm
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- Calls to limit sexual partners as mpox makes a resurgence in Australiaby /u/boppinmule on July 26, 2024 at 12:31 pm
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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 in Thailand, if your spouse cheats on you, you can legally sue their lover for damages and can receive up to 5,000,000 THB ($140,000 USD) or more under Section 1523 of the Thai Civil and Commercial Codeby /u/Mavrokordato on July 26, 2024 at 6:57 pm
submitted by /u/Mavrokordato [link] [comments]
- TIL that with a population of 170 million people, Bangladesh is the most populous country to have never won a medal at the Olympic Games.by /u/Blackraven2007 on July 26, 2024 at 6:49 pm
submitted by /u/Blackraven2007 [link] [comments]
- TIL a psychologist got himself admitted to a mental hospital by claiming he heard the words "empty", "hollow" and "thud" in his head. Then, it took him two months to convince them he was sane, after agreeing he was insane and accepting medication.by /u/Hadeverse-050 on July 26, 2024 at 6:44 pm
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- TIL Senator John Edwards of NC, USA cheated on his wife and had a child with another woman. He tried to deny it but eventually caved and admitted his mistake. He used campaign funds and was indicted by a grand jury. His life story inspired the show "The Good Wife" by Robert & Michelle Kingby /u/AdvisorPast637 on July 26, 2024 at 6:09 pm
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- TIL Zhang Shuhong was a Chinese businessman who committed suicide after toys made at his factory for Fisher-Price (a division of Mattel) were found to contain lead paintby /u/Hopeful-Candle-4884 on July 26, 2024 at 4:43 pm
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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.
- Human decision makers who possess the authority to override ML predictions may impede the self-correction of discriminatory models and even induce initially unbiased models to become discriminatory with timeby /u/f1u82ypd on July 26, 2024 at 6:29 pm
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- Study uses Game of Thrones (GOT) to advance understanding of face blindness: Psychologists have used the TV series GOT to understand how the brain enables us to recognise faces. Their findings provide new insights into prosopagnosia or face blindness, a condition that impairs facial recognition.by /u/AnnaMouse247 on July 26, 2024 at 5:14 pm
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- Specific genes may be related to the trajectory of recovery for stroke survivors, study finds. Researchers say genetic variants were strongly associated with depression, PTSD and cognitive health outcomes. Findings may provide useful insights for developing targeted therapies.by /u/AnnaMouse247 on July 26, 2024 at 5:08 pm
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- New experimental drug shows promise in clearing HIV from brain: originally developed to treat cancer, study finds that by targeting infected cells in the brain, drug may clear virus from hidden areas that have been a major challenge in HIV treatment.by /u/AnnaMouse247 on July 26, 2024 at 4:57 pm
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- Rapid diagnosis sepsis tests could decrease result wait times from days to hours, researchers report in Natureby /u/Science_News on July 26, 2024 at 3:50 pm
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Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- Charles Barkley leaves door open to post-TNT job optionsby /u/PrincessBananas85 on July 26, 2024 at 8:47 pm
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- Report: Nuggets sign Westbrook to 2-year, $6.8M dealby /u/Oldtimer_2 on July 26, 2024 at 8:13 pm
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- Dolphins signing Tua to 4-year, $212.4M extensionby /u/Oldtimer_2 on July 26, 2024 at 8:09 pm
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- Rams cornerback Derion Kendrick suffers season-ending torn ACLby /u/Oldtimer_2 on July 26, 2024 at 8:06 pm
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- Hosting the Olympics has become financially untenable, economists sayby /u/toaster_strudel_ on July 26, 2024 at 7:34 pm
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