Data leakage is one of the main reasons why ML demos look impressive... and then fail in production. ๐
The model didn't become smarter.
It just happened to see the correct answers in advance.
In 4 minutes, you'll understand where data leaks hide. ๐
Let's break it down below: ๐
1. Data Leakage ๐ณ๏ธ
Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process.
Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data.
2. Model Evaluation โ๏ธ
The test set isn't just "additional data".
It's a simulation of the future.
Only train the model on the information that would have been available to you at the time of prediction.
Evaluate it on examples that the model couldn't have influenced during training.
3. Direct Leakage ๐จ
This is the most obvious type of leakage.
Examples:
- a field with information from the future;
- an ID that encodes the target variable;
- a variable that appears only after an event has occurred;
- duplicate records in both the training and test sets.
If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage.
4. Indirect Leakage ๐ต๏ธ
This is the type of leakage that most often traps teams.
You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set.
The model didn't directly see the data from the test set.
But your preprocessing pipeline already saw it.
5. Train/Test Split โ๏ธ
Wrong:
Right:
The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data.
6. Cross-Validation ๐
Each fold is a mini-experiment with a training and test set.
Therefore, preprocessing should be performed within each fold.
If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data.
7. Pipelines ๐ ๏ธ
A pipeline isn't just a way to make the code cleaner.
It's also a defense against data leakage.
Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search).
8. AI Engineering Version ๐ค
Data leaks also occur in RAG systems and when evaluating LLMs.
Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out".
As a result, your benchmark turns into training data.
9. Leakage Checklist โ
Before trusting the obtained metric, ask yourself:
- Could this feature exist at the time of prediction?
- Was any transformation (transform) step trained (fit) on the test data?
- Did cross-validation include the entire pipeline?
- Were we tuning parameters on the final evaluation dataset?
If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model.
#MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips
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The model didn't become smarter.
It just happened to see the correct answers in advance.
In 4 minutes, you'll understand where data leaks hide. ๐
Let's break it down below: ๐
1. Data Leakage ๐ณ๏ธ
Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process.
Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data.
2. Model Evaluation โ๏ธ
The test set isn't just "additional data".
It's a simulation of the future.
Only train the model on the information that would have been available to you at the time of prediction.
Evaluate it on examples that the model couldn't have influenced during training.
3. Direct Leakage ๐จ
This is the most obvious type of leakage.
Examples:
- a field with information from the future;
- an ID that encodes the target variable;
- a variable that appears only after an event has occurred;
- duplicate records in both the training and test sets.
If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage.
4. Indirect Leakage ๐ต๏ธ
This is the type of leakage that most often traps teams.
You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set.
The model didn't directly see the data from the test set.
But your preprocessing pipeline already saw it.
5. Train/Test Split โ๏ธ
Wrong:
fit the scaler on all data โ split the data โ evaluate
Right:
split the data โ fit the scaler only on the training set โ apply it to both the training and test sets
The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data.
6. Cross-Validation ๐
Each fold is a mini-experiment with a training and test set.
Therefore, preprocessing should be performed within each fold.
If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data.
7. Pipelines ๐ ๏ธ
A pipeline isn't just a way to make the code cleaner.
It's also a defense against data leakage.
Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search).
8. AI Engineering Version ๐ค
Data leaks also occur in RAG systems and when evaluating LLMs.
Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out".
As a result, your benchmark turns into training data.
9. Leakage Checklist โ
Before trusting the obtained metric, ask yourself:
- Could this feature exist at the time of prediction?
- Was any transformation (transform) step trained (fit) on the test data?
- Did cross-validation include the entire pipeline?
- Were we tuning parameters on the final evaluation dataset?
If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model.
#MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips
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FREE MIT books on AI and Machine Learning: ๐๐ค
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems โฏ Vol 1: mlsysbook.ai/vol1/assets/do โฏ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
โฏ Part 1 : probml.github.io/pml-book/book1
โฏ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems โฏ Vol 1: mlsysbook.ai/vol1/assets/do โฏ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
โฏ Part 1 : probml.github.io/pml-book/book1
โฏ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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# Algebra 2 ๐
"Algebra 2" is another completely free textbook that covers a significant portion of algebra at both the pre-university and initial university levels. ๐
With over 1,100 pages and a large number of worked examples, practical problems, and exercises, it covers linear equations, quadratic equations, polynomial equations, rational equations, irrational equations, exponential and logarithmic equations, systems of equations, inequalities, and many fundamental concepts underlying algebra. ๐งฎ
In my opinion, this is one of the most comprehensive free resources for studying equation theory and algebraic methods typically encountered in the first years of university study. ๐ก
Source: https://openstax.org/details/books/algebra-and-trigonometry-2e
#Algebra #Math #FreeTextbook #Education #Study #University
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"Algebra 2" is another completely free textbook that covers a significant portion of algebra at both the pre-university and initial university levels. ๐
With over 1,100 pages and a large number of worked examples, practical problems, and exercises, it covers linear equations, quadratic equations, polynomial equations, rational equations, irrational equations, exponential and logarithmic equations, systems of equations, inequalities, and many fundamental concepts underlying algebra. ๐งฎ
In my opinion, this is one of the most comprehensive free resources for studying equation theory and algebraic methods typically encountered in the first years of university study. ๐ก
Source: https://openstax.org/details/books/algebra-and-trigonometry-2e
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Introduction to Deep RL and DQN
Link: https://www.dailydoseofds.com/rl-course-part-6/
๐ค #DeepRL #DQN #ReinforcementLearning #AI #MachineLearning #DataScience
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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Link: https://www.dailydoseofds.com/rl-course-part-6/
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Optimizing the model's performance through Prompt Tuning with the PEFT library.
โจ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.
๐ฆ First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
โ The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.
๐ The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.
๐ The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).
๐ Expected output: PEFT Setup: OK
๐ก Prompt Tuning โ an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.
#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โจ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.
๐ฆ First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
pip install torch transformers peft
โ The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.
from peft import PromptTuningConfig, PromptTuningInit, get_peft_model
from transformers import AutoModelForCausalLM
peft_config = PromptTuningConfig(
task_type="CAUSAL_LM",
prompt_tuning_init=PromptTuningInit.TEXT,
num_virtual_tokens=20,
prompt_tuning_init_text="Classify the sentiment of this text:",
tokenizer_name_or_path="gpt2"
)
๐ The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.
base_model = AutoModelForCausalLM.from_pretrained("gpt2")
peft_model = get_peft_model(base_model, peft_config)
peft_model.print_trainable_parameters()๐ The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).
python3 -c "from peft import PromptTuningConfig; print('PEFT Setup: OK')"๐ Expected output: PEFT Setup: OK
pip uninstall peft -y
๐ก Prompt Tuning โ an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.
#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience
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โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
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If you want to finally understand how neural networks actually learn, I recommend these notes from Stanford CS224N. ๐ง
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ๐
Inside:
โข Chain Rule
โข Computational Graphs
โข Vectorized derivatives
โข Efficient gradient calculation
โข Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ๐ฅ
These notes just fill this gap. ๐ ๏ธ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ๐
Inside:
โข Chain Rule
โข Computational Graphs
โข Vectorized derivatives
โข Efficient gradient calculation
โข Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ๐ฅ
These notes just fill this gap. ๐ ๏ธ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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Forwarded from Machine Learning with Python
Data Science Interview Questions.pdf
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Data Science Interview Questions
๐ก Here is your curated list for Data Science interviews!
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Forwarded from Machine Learning with Python
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๐ Lifetime access to HelloEncyclo โ every AI, ML & Data Science course ever built โ for ~$41. Once. Forever.
This isn't a drill. This isn't a rerun.
This is the founding-member price โ and it disappears the moment the first 250 seats globally are gone.
โ 13 courses live right now
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โ Every future course included automatically
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A new collection of free courses has been added:
๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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GitHub
GitHub - dair-ai/ML-Course-Notes: ๐ Sharing machine learning course / lecture notes.
๐ Sharing machine learning course / lecture notes. - dair-ai/ML-Course-Notes