Predictive Model

A Predictive Model is a statistical computational trained model that can solve model-based prediction tasks.

• AKA: Prediction Model, Forecasting Model.
• Context:
  • It can typically generate Predicted Values from input data using learned patterns.
  • It can typically estimate Probability Values for classification tasks.
  • It can typically identify Statistical Relationships between predictor variables and target variables.
  • It can typically support Decision-Making Processes through predictive inferences.
  • It can typically undergo Model Evaluation Tasks to assess predictive performance measures.
  • ...
  • It can often incorporate Domain Knowledge through feature engineering processes.
  • It can often require Model Retraining Tasks to maintain predictive accuracy.
  • It can often produce Confidence Scores alongside predicted outcomes.
  • It can often utilize Ensemble Methods to improve prediction robustness.
  • ...
  • It can range from being a Simple Predictive Model to being a Complex Predictive Model, depending on its model architectural complexity.
  • It can range from being a Small Predictive Model to being a Large Predictive Model, depending on its model parameter count.
  • It can range from being a Linear Predictive Model to being a Non-Linear Predictive Model, depending on its model mathematical form.
  • It can range from being a Deterministic Predictive Model to being a Probabilistic Predictive Model, depending on its model output type.
  • It can range from being an Interpretable Predictive Model to being a Black-Box Predictive Model, depending on its model transparency level.
  • ...
  • It can be produced by a Predictive Modeling System that implements machine learning algorithms.
  • It can be evaluated by a Model Performance Evaluation Task using predictive metrics.
  • It can be deployed by a Machine Learning (ML) Model Deployment System for production use.
  • It can be maintained by a Predictive Modeler through model lifecycle management.
  • It can be integrated into an Automated Inference System for real-time predictions.
  • ...
• Example(s):
  • Domain-Specific Predictive Models, such as:
    • Legal-Domain Predictive Models, such as:
      • Contract Issue-Spotting Predictive Model for legal risk identification.
      • Legal Document Classification Model for legal document categorization.
      • Case Outcome Prediction Model for litigation risk assessment.
      • Legal Compliance Risk Model for regulatory violation prediction.
    • Financial Predictive Models, such as:
      • Credit Scoring Model for credit risk assessment.
      • Financial Delinquency Predictive Model for default prediction.
    • Healthcare Predictive Models, such as:
      • Disease Prediction Model for medical diagnosis tasks.
      • Patient Risk Model for healthcare outcome prediction.
    • Business Predictive Models, such as:
      • Customer Churn Model for retention analysis.
      • Sales Forecasting Model for revenue prediction.
  • Algorithm-Based Predictive Models, such as:
    • Neural Network-based Predictive Models, such as:
      • Deep Learning Predictive Model for complex pattern recognition.
      • Convolutional Neural Network Model for image prediction tasks.
    • Tree-Based Predictive Models, such as:
      • Random Forest Model for ensemble prediction.
      • Gradient Boosting Model for high-accuracy prediction.
    • Support Vector-based Predictive Models for classification boundary learning.
  • Application-Specific Predictive Models, such as:
    • Natural Language Processing (NLP) Models, such as:
      • Named Entity Recognition (NER) Model for entity extraction.
      • Sentiment Analysis Model for opinion prediction.
    • Computer Vision Models, such as:
      • Face Recognition Model for identity verification.
      • Object Detection Model for visual recognition tasks.
    • Speech Processing Models, such as:
      • Voiceprint Recognition Model for speaker identification.
      • Speech-to-Text Model for audio transcription.
  • Behavioral Prediction Models for user behavior analysis.
  • Time Series Predictive Models for temporal pattern forecasting.
  • Collaborative Filtering Models for recommendation tasks.
  • ...
• Counter-Example(s):
  • Descriptive Models, which characterize data patterns without making future predictions.
  • Explanatory Models, which identify causal relationships rather than predictive correlations.
  • Random Number Generators, which produce output values without learned patterns.
  • Data Transformation Functions, which modify data formats without predictive capability.
  • Rule-Based Systems, which apply deterministic rules without statistical learning.
• See: Predictive Analytics, Predictive Modeling Task, Predictive Model Performance Measure, Model Evaluation Task, Machine Learning Algorithm, Statistical Model, Correlation Does Not Imply Causation, Detection Theory, Classifier (Mathematics).

---

---

References

2020

• (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Predictive_modelling Retrieved:2020-4-6.
  • Predictive modeling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place.

    In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.

    Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set. For example, a model might be used to determine whether an email is spam or "ham" (non-spam).

    Depending on definitional boundaries, predictive modelling is synonymous with, or largely overlapping with, the field of machine learning, as it is more commonly referred to in academic or research and development contexts. When deployed commercially, predictive modelling is often referred to as predictive analytics.

    Predictive modelling is often contrasted with causal modelling/analysis. In the former, one may be entirely satisfied to make use of indicators of, or proxies for, the outcome of interest. In the latter, one seeks to determine true cause-and-effect relationships. This distinction has given rise to a burgeoning literature in the fields of research methods and statistics and to the common statement that “correlation does not imply causation”.

---