John Spradlin’s Post

Residual Value, Error Term, and Endogeneity: What’re Their Importance in Machine Lesrning These concepts - #ResidualValue, #ErrorTerm, and #Endogeneity - are very important in #machinelearning and #AI, particularly in areas that involve #statistical #modelling and #causalinference. 🎁 In machine learning, #residuals are crucial for assessing model performance. They represent the difference between observed values and predicted values. Understanding residuals helps in detecting #patterns that the model hasn't captured. 🎁 The error term in statistical modelling and machine learning represents unexplained variability. It's crucial for assessing model assumptions, validating results, and improving performance. Key properties include independence, homoscedasticity, and normality. Understanding the error term aids in model selection, regularisation, and residual analysis. 🎁 Endogeneity occurs when an independent #variable is correlated with the error term, leading to #biased and inconsistent estimates. Endogeneity is particularly important in causal inference, which is a growing area of concern in AI since this is an ethnical consideration for developing unbiased #predictive models. Although machine learning often focuses on prediction rather than causal inference, understanding these concepts is crucial for interpreting models and ensuring their reliability in the context of decision-making. 👇 Check out this YouTube video by Simple Explain on Error Term: Definition, Example, and How to Calculate With Formula. https://lnkd.in/dGcw3WXB Endogeneity is a property of the true error term, not the residuals. While error terms are unobservable, residuals are observable estimates that can hint at endogeneity issues. 👇 For more, here is my post on Residual Value, Error Term, and Endogeneity: The Challenges of Causal Inference in Data Analysis. https://lnkd.in/d7PbTAGV 👆 If you find this article useful, share it in your network.

This is one of the things that was stressed in "The AI Playbook" by Eric Segal... I'd highly recommend that book if you're looking to understand why AI projects "fail to launch" and the mindset shifts that ensure the best chances that your AI project (which, BTW should be based on a business PROBLEM) will succeed. #AIPlaybook #AIBytes

🔥 How do we measure predictive model success? Spoiler: it's not just about accuracy! This video breaks down why 𝐦𝐨𝐝𝐞𝐥 𝐥𝐢𝐟𝐭 is 𝐭𝐡𝐞 metric that matters, especially when it comes to real-world business applications. Model lift compares how well your AI performs against existing logic, like boosting conversion rates with smarter next-best-offer suggestions. 📊 In our AI Bytes Newsletter (news.antics.tv), we’ve covered lift, model accuracy, and some killer reads on applying AI to business. If you’re trying to use AI practically, you won’t want to miss it. #AI #PredictiveModeling #Lift #MachineLearning #BusinessAI #DataScience #AIBytes #AIForBusiness #NextBestOffer

I'm thrilled to share my newest piece on TechTerrain, where I analyze the ongoing debate contrasting World Models with the Kalman Filter in the realm of AI. This blog post offers a comparative perspective, highlighting both similarities and differences between these two influential systems. Join me in this exploration as we contribute to the ongoing discussion on their distinct yet complementary roles in AI, control theory, and machine learning. #ArtificialIntelligence #MachineLearning #WorldModels #KalmanFilter #TechTerrain #GeospatialTechnology

Great read on Model Understanding https://lnkd.in/eXSWkfz3

A decision boundary is a conceptual dividing line or surface in a machine learning model that separates different classes or categories in a dataset. It represents the threshold at which the model makes decisions about how to classify new data points. In binary classification problems, where the goal is to classify data into one of two categories (e.g., spam vs. non-spam emails, positive vs. negative sentiment), the decision boundary is typically a line, curve, or hyperplane that separates the two classes in feature space. For example, in a simple linear classification model like logistic regression, the decision boundary is a straight line in two-dimensional space. In more complex classification problems with multiple classes, the decision boundary may be a more intricate surface or boundary that separates the different classes. For instance, in a neural network with multiple hidden layers, the decision boundary can be highly nonlinear and may consist of complex shapes or contours in feature space. The goal of training a machine learning model is to find the optimal decision boundary that minimizes classification errors on the training data while generalizing well to unseen data. The location, shape, and orientation of the decision boundary are determined by the model's parameters, which are learned from the training data during the training process. It's important to note that the decision boundary is a fundamental concept in understanding how machine learning models make predictions and classify data, and visualizing the decision boundary can provide valuable insights into the model's behavior and performance.

Self-attention is a key concept behind transformer models. If you'd like to gain a sound understanding of what it is and how it works, look no further than Bradney Smith's comprehensive explainer, which does a great job balancing accessibility and concrete detail.

Self-attention is a key concept behind transformer models. If you'd like to gain a sound understanding of what it is and how it works, look no further than Bradney Smith's comprehensive explainer, which does a great job balancing accessibility and concrete detail.

Have you heard? LAMs are coming to change how we interact with technology. Here’s the summary. Large Action Models, or Large Agentic Models (LAMs) are a type of AI model gaining popularity thanks to the Rabbit R1 keynote at CES 2024. Effectively, a LAM can take actions on a user interface, much like a user would. This differs from LLMs in that instead of textual or visual output, a LAM can comprehend parts of an interface and link them together to perform tasks in a user journey. It does this with a model that combines neural networks and symbolic reasoning. ➡ Neural networks = A computer system modelled on the human brain. ➡ Symbolic reasoning = uses symbols to represent elements and local rules to guide decisions and predictions. For example, an AI might assign symbols to represent the dates a user wants to go on holiday. It then applies logical rules to filter and recommend the available holiday packages based on those parameters. Another difference with symbolic models is that they learn from observing human interaction with a UI and then replicate the user; monkey see monkey do. This is different to learning from rules established by correlation in training, like in Machine Learning. LAMs have the potential to drastically impact the way we interact with and design interfaces, whole flows could be trained once and then completely automated. That said, with any new tech there are a host of considerations to contend with. You’d basically be giving an AI the power to act on your behalf and with that comes legal, ethical and privacy concerns. I also wonder how well it will do when platforms inevitably revamp their UI. Will the model still be able to interpret the difference? Either way, I’m sure it’ll learn quickly. The fact these models can not only think but act is a game-changer in the step to AGI. As with most new tech, it’s coming whether we like it or not. What do you think? The future of interacting with devices or another fad? Here is the Rabbit demo: https://lnkd.in/dDuMjPfV #AI #LAMs #Rabbit #CES #UX

Good and bad. We do not necessarily make decisions based on linear cause and effect. Our mind is considered a (linear)reasoning machine, but I think we overlook the biggest capability of our mind: simulations. I believe that is where our intuition comes from, it simulates factors in a probabilistic fashion and then out comes some intuitive insight. Relying solely on one-dimensional cause-and-effect analysis might limit our ability to move forward. Simulations are the way to go. https://lnkd.in/gtxU7EfR