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).


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)
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.

If you are looking for an all-in-one solution to help you prepare for the AWS Cloud Practitioner Certification Exam, look no further than this AWS Cloud Practitioner CCP CLF-C02 book

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!

 

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

  • [P] Machine Learning for Making Quotes
    by /u/Hungry-Being-2384 (Machine Learning) on February 27, 2024 at 10:32 pm

    Hey all, Im new here. Im a CompSci Graduate Student and had an interesting idea for a project, Im working on collecting data (only about 1,000 rows so far) of quoting data from a dumpster company. This includes, address, features of the dumpster (size, whether it has a lock, wheels), and their contract price. I would like to start training a model to predict what a price/quote would look like for a hypothetical instance, So if i give it size, address, etc, it would spit out the predicted price. How should i go about doing this, and any suggestions for reading and research I can do to understand this problem? Im sure the no.1 first thing i can do for better results is to gather more data. submitted by /u/Hungry-Being-2384 [link] [comments]

  • [D] How to explain my standard error of my ANN prediction
    by /u/Wrong_Entertainment9 (Machine Learning) on February 27, 2024 at 10:08 pm

    I have a question on how to satisfactorily explain the standard error of my ANN model. For background, I have an ANN model that predicts protein post translational modifications based on their GC-MS data. I got pretty good results but the standard error of the model, not the prediction is quite high. What I mean by this is that the error bars of the prediction shown in the figure below is due to plotting the model's average performance across multiple runs. How do I explain this to a reviewer of our manuscript because he still interprets the error bars stemming from the prediction, not the model's average performance. Here is the figure: gray line are the standard error submitted by /u/Wrong_Entertainment9 [link] [comments]

  • TimesFM: Google's Foundation Model For Time-Series Forecasting
    by /u/nkafr (Data Science) on February 27, 2024 at 9:59 pm

    Google just entered the race of foundation models for time-series forecasting. There's an analysis of the model here. The model seems very promising. Foundation TS models seem to have great potential. ​ ​ submitted by /u/nkafr [link] [comments]

  • Has anyone tried Santiago Valdarrama Machine Learning course?
    by /u/PinstripePride97 (Data Science) on February 27, 2024 at 9:37 pm

    This guy as course that claims that you can learn real worlds skills such as MLOps, in which if is the case i would be highly interested. Has anybody studied on his courses and if yes what was your experience like? submitted by /u/PinstripePride97 [link] [comments]

  • [R] LLM Training with > 10,000 GPUs
    by /u/CathieVictoriaWood (Machine Learning) on February 27, 2024 at 8:26 pm

    LLM Training with more than 10,000 GPUs!! https://arxiv.org/abs/2402.15627 Thoughts?? submitted by /u/CathieVictoriaWood [link] [comments]

  • [D] Tuning parameters of autoencoder for deep representation and K-means clustering
    by /u/Naive-Bee4750 (Machine Learning) on February 27, 2024 at 7:43 pm

    Hey everyone. I'm using deep representation and K-means clustering to find structures in unlabeled single particle trajectory data. I start with a sequence of shallow features, use a deep sparse autoencoder to get deep features, use them as input for the K-means clustering and evaluate the Davies-Bouldin index. Now I want to tune the parameters of the autoencoder (sparsity target, weights and learning rate). Would it make sense to create a parameter grid, and for each combination evaluate the clustering performance and select the parameters that in the end minimize DBI? Any other suggestions? submitted by /u/Naive-Bee4750 [link] [comments]

  • Is developing on Apple’s M1 and M2 chips still a pain?
    by /u/Owz182 (Data Science) on February 27, 2024 at 7:37 pm

    Had a colleague with an M1 chip and every time we shared docker containers we’d have to go back and forth figuring out why the combination of libraries in my container would upset his M1 chip MacBook. Well now I’m changing jobs and they’ve offered me an M2 chip MacBook. Do those issues still exist? Am I better off requesting a 2019 MacBook with an Intel chip? submitted by /u/Owz182 [link] [comments]

  • sdmetrics: Library for Evaluating Synthetic Data
    by /u/semicausal (Data Science) on February 27, 2024 at 7:28 pm

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

  • [D] Advice for the next step of researchers (postdoc/PhD) who do interdisciplinary AI
    by /u/CurrentExamination49 (Machine Learning) on February 27, 2024 at 6:53 pm

    I am not sure if it’s a common question. I am a postdoc doing projects related to AI in Europe, no papers in top-tier AI conferences. I mainly focus on applying AI to domain-specific datasets. However doing a postdoc is an unstable job and I realized that I might not like doing research, maybe due to lacking interest and passion for research. Besides keeping doing Postdoc or applying PI (could be very challenging). Is industrial an option for my case? Obviously, I can’t compete with "actual AI" researchers to get into big tech companies. What about MLE or DS jobs for smaller tech companies? Also without New Graduate, would finding these jobs be even harder for me? If it’s possible, what should I prepare? Leetcode or something. If not what could be the options? Thanks. submitted by /u/CurrentExamination49 [link] [comments]

  • [D] Capturing forecast uncertainty for a SVR model
    by /u/LDM-88 (Machine Learning) on February 27, 2024 at 6:35 pm

    Does anybody have any suggestions to quantify forecast uncertainty for an SVR model? In parametric models such as basic OLS - forecast intervals accounting for both parameter and observational uncertainty is fairly straightforward. Similarly with a neural network - uncertainty can be quantified by adding dropout layers and modelling the distribution of predictions - however I'm not entirely sure how best to do this using an SVR! Any ideas greatly welcome submitted by /u/LDM-88 [link] [comments]

  • [P][R] IdentitiySimilarity (idsim) package can be used for your face related researches.
    by /u/m-pektas (Machine Learning) on February 27, 2024 at 6:28 pm

    I am sharing the IdentitiySimilarity python package with you. This package can help researchers who want to use face recognition in their research. You can easily implement powerful face recognition models in your project. I was inspired by LPIPS for this repository. Thanks to your contribution with your codes or comments, it can be a more useful repository. submitted by /u/m-pektas [link] [comments]

  • [P][R] IdentitiySimilarity (idsim) package can be used for your face related researches.
    by /u/m-pektas (Machine Learning) on February 27, 2024 at 6:27 pm

    I am sharing the IdentitiySimilarity python package with you. This package can help researchers who want to use face recognition in their research. You can easily implement powerful face recognition models in your project. I was inspired by LPIPS for this repository. Thanks to your contribution with your codes or comments, it can be a more useful repository. submitted by /u/m-pektas [link] [comments]

  • [D]Recent literature related to Convex Optimization?
    by /u/Quiet_Cantaloupe_752 (Machine Learning) on February 27, 2024 at 6:27 pm

    Hi all, I am in a convex optimization class, and a key component of the class is a project in which we relay convex optimization back to our area of study, which for me is deep learning. Obviously this could also transform into a research idea if significant progress is made. Anyways, I’m looking for direction/suggestions on recent papers/interesting projects I could explore. I do hope to present some degree of novelty in my results! Thanks in advance submitted by /u/Quiet_Cantaloupe_752 [link] [comments]

  • With what technique would you tackle this puzzle (free flow)? More info in comments. [D]
    by /u/IntroDucktory_Clause (Machine Learning) on February 27, 2024 at 6:17 pm

    ​ https://preview.redd.it/7nt5nhtd66lc1.png?width=497&format=png&auto=webp&s=0cbf2001eb6add8107e2d99be76e185c533d9249 submitted by /u/IntroDucktory_Clause [link] [comments]

  • Is this a fucking joke ?
    by /u/longgamma (Data Science) on February 27, 2024 at 5:51 pm

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

  • [D] dataloading making my code slower
    by /u/bkffadia (Machine Learning) on February 27, 2024 at 5:27 pm

    I have sequential data stored in h5 file and I am using pytorch dataloader and a custom class I created myself class H5Dataset(data.Dataset): def __init__(self, path): super(H5Dataset, self).__init__() self.path = path self.file = h5py.File(path, 'r') self.keys = list(self.file['Labels/Training/'].keys()) def __getitem__(self, index): key = self.keys[index] key2 = key.replace('L', 'I') key2 = 'Input/' + key2 key = 'Labels/Training/' + key inputs = np.array(self.file[key2]) inputs = torch.tensor(inputs) labels = self.file[key] labels = np.array(labels) labels = torch.tensor(labels) return inputs, labels.float() def __len__(self): return len(self.keys) when I try to use num_workers >1 in dataloaders I get an error message saying that h5 objects cannot be pickeled, and I think dataloading is the bottleneck since I'm using only 66% of the GPU. any advices about how to make it faster ? submitted by /u/bkffadia [link] [comments]

  • [p] Alma doesn't translate the next row
    by /u/HackerPigeon (Machine Learning) on February 27, 2024 at 5:26 pm

    I know little about how to train a lora llm , I'm trying to follow ALMA , https://github.com/fe1ixxu/ALMA for translation, I used the alpaca dataset structure as llama factory says ....the model surprisingly works but it doesn't go to next row , as soon at there is a dot and a next row it doesn't translate it ? Is it something that has to do with the inference ? With the training ? With the dataset ? submitted by /u/HackerPigeon [link] [comments]

  • [D] Isn't the idea of "generalizing outside of the distribution" in some sense, impossible?
    by /u/EveningPainting5852 (Machine Learning) on February 27, 2024 at 5:21 pm

    Hey, I'm not a ML guy specifically but I'm a dev and have a 8yr long hobbyist research into this. The idea of generalizing outside of the distribution has always been quite unusual to me. Of course, if you learn English, you will now in some sense, know how to code, since it is in English. The model has not generalized nearly as much as we hope in this case because the base knowledge required to code isn't logic nearly as much as it's literacy. In the same sense, one can imagine a color that is an amalgamation of different colors. But imagining a brand new color, go ahead and give it a try, is literally impossible. In this case our definition of generalizing outside of the distribution is not outside of it, it's just that the distribution was bigger than we thought (or could quantify) Same thing with imagining a sound you've never heard. Once again, you can potentially imagine an amalgamation of other sounds you've heard, and perhaps this new sound you've imagined truly is something you've never heard in reality, but you haven't generalized into the rest of the spectrum. I can't imagine what a sound above 20khz sounds like because I have absolutely no ground truth to what that could be, just like I can't imagine objectively what an x-ray looks like because I'm restricted by my ability to only see visible light. submitted by /u/EveningPainting5852 [link] [comments]

  • [P] TIME series forecasting Index based on Big players in market
    by /u/Ok_Outcome_600 (Machine Learning) on February 27, 2024 at 5:02 pm

    this is basic model and this model is performing very well if you read the metrics (how is performing in real world) https://www.reddit.com/r/NSEbets/s/4MUDry4m7Y, I have FNO segment data(high risk financial instrumental like option chain expiry wise, and future monthly wise), I have cleaned the data, and I want to feed it parallel so, they can find there average position of Big player (like DII domestic institution invester and FII Foreign institutional invester)and we can find historical pattern in options and futures this is just overhaul, I have analyze that it is so much powerfull, if we feeded that all data in parallel, I tried with unique date indexing, but have more data, if add more and more features/columns that will be impractical Any suggestions how can feed that data in parallel I have 5-7 type of Different data submitted by /u/Ok_Outcome_600 [link] [comments]

  • [D] Exploring GPT-4's Chess Capabilities: Is There an Instruct Model Comparable to GPT-3.5 Turbo?
    by /u/dewijones92 (Machine Learning) on February 27, 2024 at 4:41 pm

    I recently came across an intriguing post about GPT-3.5 Turbo-Instruct's ability to play chess remarkably well (see the previous discussion here: https://old.reddit.com/r/GPT3/comments/16mefly/the_new_gpt_model_gpt35turboinstruct_can_play/). It's fascinating to see such proficiency in chess from a general AI model that isn't specifically designed for the game. This revelation leads me to ponder whether there's an equivalent "Instruct" version for GPT-4, and if so, how it measures up in terms of chess-playing capability, perhaps in terms of its ELO rating. Any insights or information on this would be greatly appreciated. Thanks! submitted by /u/dewijones92 [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?

error: Content is protected !!