John Penberthy-Smith’s Post

Bias–Variance Tradeoff in Machine Learning Why does a model perform well on training data but fail on new data? The answer often lies in the bias–variance tradeoff. 🔹 Bias measures how much a model’s assumptions differ from the real patterns in the data. A model with high bias is usually too simple. It fails to capture important relationships and leads to underfitting. 🔹 Variance measures how much a model’s predictions change when it is trained on different datasets. A model with high variance is usually too complex. It learns noise and minor details in the training data, leading to overfitting. The expected prediction error can be summarized as: Total Error = Bias² + Variance + Irreducible Error The goal is not to eliminate bias or variance completely. The goal is to find the right balance: High bias + low variance → Underfitting Low bias + high variance → Overfitting Balanced bias and variance → Better generalization Common ways to manage this tradeoff include: ✅ Using cross-validation ✅ Applying regularization ✅ Selecting the right model complexity ✅ Collecting more training data ✅ Performing feature selection ✅ Using ensemble learning ✅ Monitoring training and validation errors A good machine learning model is not the one that memorizes the training data. It is the one that performs reliably on data it has never seen before. #MachineLearning #ArtificialIntelligence #DataScience #BiasVarianceTradeoff #Overfitting #Underfitting #ModelEvaluation #DeepLearning #AI

Zero Shot ML or Tabular ML - This is the most exciting update since AutoML in Machine learning. Based on recent releases by Google Research - TabFM and TimesFM- these meta-learners are magical, and you can naturally run Machine Learning via chat. Just ask your favorite agent to do all sorts of forecasting and predictions. These models are the numerical equivalent of Generative AI. They can run Regression, Classification, and Time Series Forecasting within Context and within your agentic loop. No Training, No Features. Nothing. #semanticLayer #IncontextLearning #ZeroShotML https://lnkd.in/e3YS9Fjw

I've worked on several AI/Data consultant projects and I believe the biggest mistake people make when they use machine learning for scientific data is using machine learning before fully understanding the scientific question and the quality of the data. Many researchers request machine learning because it is popular, even when a simpler statistical model would answer their research question more effectively. Just my two cents but consultants should first identify the real problem rather than immediately accepting the requested method. A client may ask for machine learning when the true goal is simply risk prediction or identifying important factors. Another common mistake is overfitting, where the model learns random noise instead of meaningful scientific patterns, resulting in poor performance on new data. Overfitting can be reduced through appropriate regularization techniques and proper model validation. Finally, no machine learning model can compensate for poor-quality data, the principle of "garbage in, garbage out" still applies. As we work with the projects, our responsibility is to ensure that the research question, study design, and data quality are appropriate before choosing any analytical method. #MachineLearning #DataScience #ArtificialIntelligence #StatisticalConsulting #Statistics #ResearchMethods #ScientificResearch #DataQuality #StudyDesign #ModelValidation #PredictiveModeling #EvidenceBased #Biostatistics #ClinicalResearch #Analytics

Day 5/30 – Data Preprocessing & Train-Test Split One observation that has remained consistent across almost every machine learning project I've worked on is: Most model performance issues originate long before the first algorithm is trained. We often invest significant effort comparing algorithms or tuning hyperparameters, yet overlook the quality of the data being used. Raw datasets are rarely ready for modelling. Missing values, inconsistent formats, duplicate records, outliers, and poorly scaled features are not just data quality issues—they directly influence what the model learns. In my experience, improving the dataset almost always has a greater impact than replacing one algorithm with another. Equally important is how we evaluate the model. One of the easiest ways to overestimate a model's performance is to evaluate it on the same data it was trained on. The resulting metrics may look impressive, but they tell us very little about how the model will behave in production. This is why the train-test split is fundamental. The training set allows the model to learn patterns, while the test set answers the only question that really matters: Can the model make accurate predictions on data it has never seen before? We are not trying to build models that memorize historical observations. We are trying to develop models that generalize. I've found that the strongest machine learning solutions rarely come from using the most sophisticated algorithm. They come from careful data preparation, thoughtful experimental design, and rigorous evaluation. Because in the end, a model can only be as reliable as the data it learns from and the methodology used to validate it. #30DaysOfML #MachineLearning #DataPreprocessing #TrainTestSplit #DataScience #ArtificialIntelligence #Research #LearnInPublic #AI

📘 Data Science Concepts | Day 7 Today's concept: Training Data vs Test Data Training data teaches the model. Test data evaluates the model. A simple concept, but fundamental to building reliable Machine Learning models. #DataScience #MachineLearning #ModelEvaluation #AI

🚀 Google Research introduced TabFM, a new foundation model for tabular data. For years, AI excelled at text and images, but tabular data (spreadsheets, databases, CSVs) was still dominated by XGBoost, LightGBM, and CatBoost. TabFM takes a different approach. Instead of training a new model for every dataset, it uses In-Context Learning (ICL). Simply provide historical labeled data along with new rows, and the model predicts the results—without traditional training or hyperparameter tuning. 💡 Why it stands out: • Hybrid attention designed for tables • Pre-trained on millions of synthetic datasets • Scales to large enterprise datasets Why it matters ✅ Faster predictive analytics ✅ Less feature engineering and tuning ✅ Brings foundation models closer to structured business data TabFM doesn't replace traditional tree-based models overnight, but it marks an exciting step toward making AI-powered prediction faster and more accessible. What are your thoughts? Could foundation models become the future of tabular machine learning? #AI #MachineLearning #DataScience #GoogleResearch #TabFM #ArtificialIntelligence #BigQuery

📊 Why do we split a dataset into Training, Validation, and Test sets? When building a machine learning model, getting a high accuracy on your dataset is not enough. The real question is: How well will the model perform on data it has never seen before? That’s why we divide the data into three parts 👇 🔹 Training Set — Learn This is where the model learns patterns from the data. Its parameters are updated based on the examples in the training set. 🔹 Validation Set — Improve The validation set helps us make decisions during development, such as: • Choosing hyperparameters • Comparing different models • Selecting the best architecture • Deciding when to stop training It helps us improve the model without directly training it on the validation data. 🔹 Test Set — Evaluate The test set should be kept separate until the model is completely finalised. It provides the best estimate of how the model is likely to perform on genuinely unseen data. ⚠️ One important rule: Never use the test set to tune your model. If you repeatedly adjust your model based on test results, the test data is no longer truly "unseen." This can lead to overly optimistic performance results. 💡 A simple way to remember it: Training = Learn Validation = Decide Testing = Prove Keeping these three stages separate helps us build models that are not only accurate on the data we have, but also more reliable when they encounter new data in the real world. #MachineLearning #AI #DataScience #DeepLearning #ArtificialIntelligence #ML #DataAnalytics

📊 Small datasets? Here's how to get reliable ML results—fast. One of the biggest myths in machine learning is that you need millions of rows to build a useful model. The reality? With the right approach, even small datasets can produce reliable, production-ready models. Here are three tactics that consistently deliver better results: ✅ 1. Feature Engineering Don't just collect more data—extract more value from what you already have. Domain-specific features, interaction terms, aggregations, and meaningful transformations often improve performance more than switching algorithms. ✅ 2. Cross-Validation A single train/test split can be misleading with limited data. Use K-Fold Cross-Validation to get a more stable estimate of model performance and reduce the risk of overfitting. ✅ 3. Synthetic Data Augmentation When appropriate, carefully generate additional training samples using techniques like SMOTE (for tabular data) or augmentation strategies for images and text. More balanced data often leads to more robust models. A simple rule of thumb 📌 The smaller your dataset, the simpler your model should be. A well-tuned, interpretable model on quality features will often outperform a complex deep learning model trained on limited data. One evaluation mistake that catches teams off guard 🚨 Data leakage. If information from the validation or test set accidentally influences training, your accuracy can look amazing—until deployment. Quick check: Make sure every feature would actually be available at prediction time, and verify that preprocessing, scaling, and feature selection are performed after the train/validation split. Reliable ML isn't about having the most data—it's about making the most of the data you have. 💬 How large is the dataset you're currently working with? Share the approximate size in the comments, and I'll suggest techniques that fit your use case. #MachineLearning #DataScience #AI

Machine Learning as Behavior Emulation: In my experience as an AI consultant / mentor, I have discovered that shifting the perspective from the traditional view of Data Science to Behavioral Emulation has made the learning experience more compelling, intuitive and easier to grasp. When data is viewed not as a static collection of numbers but as "fossilized behavior" - traces that are left behind by dynamic systems, modeling becomes an exercise in emulating the internal logic of those dynamic systems. Machine Learning ceases to be just about building powerful predictive machines. Instead, it becomes an exercise in "digital empathy," where we build agents that don't just calculate, but emulate the very essence of the systems we are studying. Data as Behavior: Standard Machine Learning teaches that a dataset is a static collection of numbers - kinda like a collection of inputs (X) and outputs (y). Data is not static; it is a trace. It is the footprint left behind by a dynamic entity - a human, a market, a biological system, acting in the world. When you look at a dataset of credit card transactions, you are not looking at just currency. You are looking at human desire and risk encoded as numbers. When you look at pixel data, you are looking at the physics of light interacting with objects. But Data is lossy. It is a low-resolution shadow of the complex behavior that generated it. Read Further: https://lnkd.in/e5-faQBf

Many students tell me: SAI DATASCIENCE “There are so many AI tools. Which ones do I actually need to learn?” I created this student-friendly AI ecosystem map to make the landscape easier to understand. You do not need to learn every tool. Start with: One foundation model One RAG framework One vector database One evaluation tool One automation platform Learn the purpose first—then choose the tool. Which part of the AI ecosystem would you like me to explain next? Follow me for practical lessons in AI, data science and career development. (https://saidatascience.com) #ArtificialIntelligence #GenerativeAI #DataScience #AIAgents #CareerDevelopment #SAIDataScience