predictive modeling

(noun)

Predictive modelling is the process by which a model is created or chosen to try to best predict the probability of an outcome.

Related Terms

Examples of predictive modeling in the following topics:

  • The Equation of a Line

    • In statistics, linear regression can be used to fit a predictive model to an observed data set of $y$ and $x$ values.
    • In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable.
    • This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.
    • If the goal is prediction, or forecasting, linear regression can be used to fit a predictive model to an observed data set of $y$ and $X$ values.
    • After developing such a model, if an additional value of $X$ is then given without its accompanying value of $y$, the fitted model can be used to make a prediction of the value of $y$.

  • Modeling Ecosystem Dynamics

    • Conceptual models describe ecosystem structure, while analytical and simulation models use algorithms to predict ecosystem dynamics.
    • These models predict how ecosystems recover from disturbances, returning to a state of equilibrium.
    • They are mathematically complex models that are good at predicting components of ecosystems such as food chains.
    • Like analytical models, simulation models use complex algorithms to predict ecosystem dynamics.
    • However, sophisticated computer programs have enabled simulation models to predict responses in complex ecosystems.

  • Introduction to explaining the relations among actors in a network

    • Rather than building a statistical model to predict each actor's out-degree, we could, instead, predict whether there was a tie from each actor to each other actor.
    • In this final section, we will look at several statistical models that seek to predict the presence or absence (or strength) of a tie between two actors.
    • Several of the models below explore homophily and closeness to predict whether actors have ties, or are close to one another.
    • The last model that we will look at the "P1" model also seeks to explain relations.
    • The P1 model tries to predict whether there exists no relation, an asymmetrical relation, or a reciprocated tie between pairs of actors.

  • A single-variable model for the Mario Kart data

    • Let's fit a linear regression model with the game's condition as a predictor of auction price.
    • The model may be written as:
    • Interpret the coefficient for the game's condition in the model.
    • So 10.90 means that the model predicts an extra $10.90 for those games that are new versus those that are used.
    • Summary of a linear model for predicting auction price based on game condition.

  • Using the Model for Estimation and Prediction

    • The output would also tell us if the model allows the prediction of a person's height at a rate better than chance.
    • This is denoted by the significance level of the model.
    • In other words, the model is fairly good at predicting a person's height, but there is between a 5-10% probability that there really is not a relationship between height, weight and gender.
    • In addition to telling us the predictive value of the overall model, standard multiple regression tells us how well each independent variable predicts the dependent variable, controlling for each of the other independent variables.
    • Analyze the predictive value of multiple regression in terms of the overall model and how well each independent variable predicts the dependent variable.

  • Model selection exercises

    • Exercise 8.3 considers a model that predicts a newborn's weight using several predictors.
    • Exercise 8.4 considers a model that predicts the number of days absent using three predictors: ethnic background (eth), gender (sex), and learner status (lrn).
    • Exercise 8.3 provides regression output for the full model (including all explanatory variables available in the data set) for predicting birth weight of babies.
    • Exercise 8.4 provides regression output for the full model, including all explanatory variables available in the data set, for predicting the number of days absent from school.
    • We should consider removing this variable from the model.

  • Studying Ecosystem Dynamics

    • Three basic types of ecosystem modeling are routinely used in research and ecosystem management: conceptual models, analytical models, and simulation models.
    • A conceptual model describes ecosystem structure and dynamics and shows how environmental disturbances affect the ecosystem, although its ability to predict the effects of these disturbances is limited.
    • Analytical and simulation models are mathematical methods of describing ecosystems that are capable of predicting the effects of potential environmental changes without direct experimentation, although with limitations in accuracy.
    • An analytical model is created using simple mathematical formulas to predict the effects of environmental disturbances on ecosystem structure and dynamics.
    • A simulation model is created using complex computer algorithms to holistically model ecosystems and to predict the effects of environmental disturbances on ecosystem structure and dynamics.

  • Essential Functions for Mathematical Modeling

    • Mathematical models are used to explain systems, study effects of components, and make predictions about behavior.
    • The process of developing a mathematical model is termed mathematical modeling.
    • A model may help to explain a system and to study the effects of different components, and to make predictions about behavior.
    • Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models.
    • For example, a simple model of population growth is the Malthusian growth model.

  • Introduction to Model selection

    • The best model is not always the most complicated.
    • Sometimes including variables that are not evidently important can actually reduce the accuracy of predictions.
    • In this section we discuss model selection strategies, which will help us eliminate from the model variables that are less important.
    • In this section, and in practice, the model that includes all available explanatory variables is often referred to as the full model.
    • Our goal is to assess whether the full model is the best model.

  • Predicting Vignette

    • The students quickly answer, "She is predicting the weather."
    • I am going to model for you."
    • Several times in the story, she stops and makes changes in her predictions or points out that her predictions were correct based on what she has read so far.
    • Clark make better predictions.
    • The changed prediction is recorded on the graphic organizer.