"Science describes things as they are, constructs predictive models, not absolute claims of truth."
"Most economic modelling has found that a Brexit vote would make little difference to Britain's economy."
"Using supercomputers, scientists not only study the past, but they can predict the future of the universe."
"The goal of supervised learning is always to build a model that generalizes to out-of-sample data."
"Causal inference tries to go a step beyond predictive modeling and help stakeholders understand why something's happening."
"AI will play a crucial role in Hypersonic flight control and prediction."
"A lot of machine learning is about extracting good features from the world I'm going to go for over another five minutes and we'll take a 10-minute break I'm extracting good features from the world to solve predictive tasks decision tasks as well."
"The ability to make models of the future is the prerequisite for agency."
"Regression to the mean is a good thing statistically; it improves your predictions."
"Second, we'll read in and clean the data. Third, we'll train a machine learning model to make historical and future predictions for our data."
"...the model offers quantitative testable predictions."
"When you're creating features, only use data that you actually will know at the time of prediction."
"Motion generation using model predictive control."
"Weak learners can be used to make strong predictions which was the big takeaway of the paper."
"80% of predicting prices is done with less than five or eight factors."
"It enables us to predict how the environment in which we're testing is gonna work and it takes away one point of failure that might be completely unrelated to what you're trying to test."
"Physics-informed AI can learn to emulate physics and make predictions that obey physical laws."
"Create a model and then we want to test that model predicting whether somebody new has diabetes or not."
"When a model fits the training data perfectly, it probably means it is overfit and will not perform well with new data."
"Predictive analytics understands the future to answer what could happen."
"We're having a search phrase, we're having some product IDs, we're having a total number of clicks, and that number, the total number of clicks, is what we're trying to predict."
"If we understand how the system behaves based on the data well enough to inform a model of how the system is behaving, we can perhaps predict how the system will behave in circumstances we haven't yet observed."
"We build a single model at the start. We use it to make predictions on later data, the model accuracy can kind of degenerate over time as that data changes."
"What does this data and the model associated with it tell me about future expectations, help me predict other results that will come out of this data."
"One of the things we care about when we use machine learning is not only our ability to make predictions with the model but what can we learn by studying the model itself."
"Precision means among the examples that we predicted to be positive, what fraction of them were actually positive."
"We do what we call full stack end-to-end data science which is all the way from data management to developing predictive models to building web applications."
"An endogenous variable is just a variable that's being predicted by another variable within the model."
"Our prediction only depends on the sum of several different feature functions."
"Data analytics solutions let organizations improve business outcomes by allowing them to identify patterns and build predictive models around them."
"You've successfully created a machine learning model to predict the price of cars and automobiles based on numerous features."
"Selecting the correct predictive fields for your target field is crucial."
"Word2Vec is a predictive framework where it takes in a sequence and it tries to predict some subset of that sequence with a very simple linear model."
"During the testing phase, when we have a sample point, the goal is now to compute the label Y."
"This task is called selecting features to build a mathematical predictive model that can be used to make predictions about a phenomenon of your choice."
"When we build a predictive model, we expect the model to work or to perform well in the new data."
"Kaggle is a good way to get better at attacking predictive modeling problems."
"We're building a predictive model to clean missing data so that we can actually get a more accurate predictive model on all of it."
"These models that learn the shape of the prior from the sample because they make better estimates for better predictions and better predictions of causal effects as well."
"Predictive modeling, great machine learning skill, data visualization... just the common graphs and that kind of way to understand what data looks like is a very important skill to build."
"...our accuracy is 94% so 94 out of 100 students were correctly predicted to be admitted or not by our model."
"We're done with the model building and the accuracy part, so let's build a predictive system now."
"The goal is to learn a function H such that h of X is equal to Y."
"We construct models and start making predictions; it gives us useful information used in business all the time as well as psychology."
"The target is survive, so we want to predict whether people are surviving, essentially, the crash of the Titanic."
"When you have very large data sets... it's a good idea to run EDA, find which variables are most important for prediction."
"Supervised learning is creating a predictive model based on a set of features and labels."
"The sweet spot for any model is the level of complexity at which the increase in bias is equivalent to the reduction in variance."
"Filter feature selection methods... for each feature, compute some measure of how informative xi is about y."
"With R, you can get very deep; you can basically create statistical models for predictive analysis."