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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.

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.
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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!
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
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I work on a trading desk and right now they've mainly been asking me to do more data engineering and ad hoc analysis work. There was a past modeling project with some regressions written in R. I'm basically the only data scientist/technologist on the trading desk, but I have a lot of flexibility in terms of what I can work, but I would just need to justify in terms of providing business impact. How do you justify investing time into machine learning/modeling projects to your business stakeholders? submitted by /u/Ambitious-Wolf-2439 [link] [comments]
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I'm needing some extra money and I could certainly use a second income right now. However, I know nothing about freelancing besides that some platforms help with that like fiverr and upwork. Should I simply subscribe in one of these platforms? Should I try to start on linkedin? I'd like to work with the delivery of data analysis projects with AI models or dashboard creation with Power BI (these are my main skills). Any tips? submitted by /u/Traditional-Reach818 [link] [comments]
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How many features are typically used in a model? In my work, for example, using XGBoost, my boss selects the top 40 most important features based on a certain score, and we use between 30 and 20 of those features. The minimum number of features we have used is 16. While using 20 features doesn't seem to cause overfitting, it still feels like a lot. And also how can i deal with a client that ask to include 30 variables? submitted by /u/bebu17 [link] [comments]
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My company is offering to pay my way to a conference or workshop or similar to advance my skills. Where should I go? We're in the healthcare space. I'm sort of a jack-of-all-trades so I'm open to a lot of stuff. Perhaps optimization would be a good area, or generative neural networks. What are the biggest & best overall DS conferences? Healthcare specifically? Is there a good GNN conference/workshop? submitted by /u/GlitteringBusiness22 [link] [comments]
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Regardless of its change in course from where it started, when I compare the leadership at openAI to other big tech businesses, I think we lucked out with openai. I see lots of hate for sam and openAI online. TBH it's a matter of time before someone goes and does something like a network of semi-autonomous auto-GPT's planning and executing all sorts of chaos/attacks so I think getting ahead of things like this and talking about some type of regulation is perfectly warranted. Also I don't know if you listened to the court hearing but he specifically said that the regulation needs to be focused on Google, Microsoft, and openAI and other large competitors rather than open source (ofc open-source will be affected). Although I don't want heavy regulation, it seems like a lot of people want almost no regulation which is very odd to me. (Also bringing competition to google is a huge bonus) submitted by /u/Initial-Doughnut-765 [link] [comments]
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Hello, I am currently an MLOps engineer in my company. I help data scientists create tools and assist with deployment problems. Perhaps I have come to hate learning DevOps stuff, such as Terraform, data permissions, security, etc. Initially, my first idea was to work as a data scientist. However, I gave up on that idea since I have ADHD (although I didn't know it at the time). However, five months ago, I started treatment for my ADHD problem, and I began to gain confidence. Today, I spoke with my boss, and he said I can transition to becoming a data scientist. However, it will take some time because I am not too good with business-related matters, and I need to improve my communication skills. Due to my ADHD, I have always struggled with impulsive problems in my speech. I have also been thinking about trying to apply for jobs. However, I am not currently confident enough to do so. submitted by /u/Muted_Standard175 [link] [comments]
- Considering doing a career transition for Software Engineer or Data Engineer for more remote opportunities. Thoughts?by /u/CadeOCarimbo (Data Science) on May 29, 2023 at 9:22 pm
I'm a data Scientist with 6+ years of experience. Doing this shift is something that has crossed my mind a few times in the last 2 years but now it seems the right time for me to start a study plan to accomplish this. I like both the stats and the programming part of Data Scientist but I'm getting quite frustrated at the lack of remote job opportunities for Data Scientist. It seems many company think that DS should be as close as possible to the business people, hence enforcing RTO. With devs and data engineers it seems much there are many more WFH from home, even for companies outside of my country (Brazil). Thoughts on doing this transition? What's a study plan like to becoming a data or software engineer given my technical background? submitted by /u/CadeOCarimbo [link] [comments]
- How to develop better observation skill for data analysis?by /u/Devilinyou_666 (Data Science) on May 29, 2023 at 9:05 pm
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- Would Masters from American/Canadian University help me find a better opportunity?by /u/ExcuseNo6720 (Data Science) on May 29, 2023 at 8:31 pm
Hi, I am a new immigrant to Canada who is trying to find Data Scientist opportunities in this brutal market. I have around 4+ years of relevant experience and a Master's degree in engineering from my home country under my belt. I am working at a small company currently. However, I am finding it hard to move to a better/bigger opportunity. I was able to get to the final rounds of interviews at the handful of companies and at least 3 of the opportunities I lost to folks having Master's from Canadian University which makes me wonder if I should go back to university. At the same time I am not sure whether I am ready for such big commitment (time and finance wise) at this point in my life. I see these online Master's programs. Can someone tell me how valuable are they in the job market? submitted by /u/ExcuseNo6720 [link] [comments]
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- [R] Machine Learning for Ancient Languagesby /u/yannisassael (Machine Learning) on May 29, 2023 at 7:42 pm
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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?


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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
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- Ellis Horowitz - Fundamentals of Computer Algorithms
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- 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
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- 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
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- 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
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- Effective Java 2nd edition
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- The Seasoned Schemer
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- 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)
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- Refactoring to Patterns by Joshua Kerievsky
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- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
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- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
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- Growing Object-Oriented Software, Guided by Tests
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- 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
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- 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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