You can try to read article by Ishan Kapoor, he wrote about how to seperate predictive models from generative: https://www.linkedin.com/pulse/predictive-ai-vs-generative-unveiling-distinctions-ishan-kapoor/

For me, I understand them as regression and classification models. At the same time I understand generative models and models which can create something (text, image and so on).

In general, many methods can be used to create both.

Predictive models in AI are algorithms or systems that use historical data to predict future results or make estimates of events or behaviors. These models are trained using data sets that contain information about relevant variables and their corresponding results or labels.

Predictive models can be used in a wide variety of applications, such as predicting the behavior of the financial market, predicting the weather, predicting the risk of diseases, anticipating consumer behavior, among others.

Some examples of predictive models in AI include linear regression, logistic regression, decision trees, neural networks, support vector machines (SVMs), among others. These models use different techniques and algorithms to analyze the data and generate predictions or estimates.

The most obvious advantage of predictive modeling is that it can help you make better future decision-making processes based on assembling all the pertinent data on previous use cases to improve your decisions going forward. In other words, by training a network with input/output data and then testing it after that this network can be used with different input data and maintain the concept of the previous output. However, it cannot be confirmed 100% working properly.

Predictive AI models are a type of artificial intelligence (AI) that is used to forecast future outcomes based on historical data and patterns. These models are trained on large datasets of past events and outcomes, and they use machine learning algorithms to identify trends and correlations in the data. Once trained, predictive AI models can be used to make predictions about future events, such as whether a customer is likely to churn, whether a patient is likely to develop a certain disease, or whether a machine is likely to fail.

Predictive AI models are used in a wide range of industries and applications, including:

• Business: Predictive AI models can be used to forecast sales, predict customer churn, and identify new market opportunities.
• Finance: Predictive AI models can be used to assess risk, detect fraud, and make investment decisions.
• Healthcare: Predictive AI models can be used to diagnose diseases, predict patient outcomes, and develop personalized treatment plans.
• Manufacturing: Predictive AI models can be used to forecast demand, optimize production schedules, and predict machine failures.
• Security: Predictive AI models can be used to detect cyber threats, prevent fraud, and predict terrorist attacks.

Predictive AI models are a powerful tool that can be used to improve decision-making and outcomes in a wide range of industries and applications. However, it is important to note that predictive AI models are only as good as the data they are trained on. If the data is biased or incomplete, the model will produce biased or inaccurate predictions. It is also important to monitor predictive AI models regularly and to update them with new data as it becomes available.

Here are some examples of predictive AI models in use today:

• Netflix uses predictive AI models to recommend movies and TV shows to its users.
• Amazon uses predictive AI models to recommend products to its customers and to forecast demand for products.
• Google uses predictive AI models to improve the accuracy of its search results and to predict traffic congestion.
• Banks use predictive AI models to assess creditworthiness and to detect fraud.
• Hospitals use predictive AI models to diagnose diseases and to predict patient outcomes.

Predictive AI models, often referred to as predictive analytics models, are a subset of artificial intelligence (AI) models designed to forecast or predict future outcomes based on historical data and patterns. These models use machine learning algorithms to analyze large datasets, identify trends, and make predictions or recommendations. Predictive AI models find applications in various fields, including finance, healthcare, marketing, and more. Here are key characteristics and components of predictive AI models:

Data Input: Predictive AI models require historical data as input. This data typically includes relevant features, variables, or attributes that may influence the predicted outcome. Data can be structured (e.g., tables) or unstructured (e.g., text or images).

Training Data: To build a predictive model, a portion of the historical data is used for training. During training, the model learns patterns, relationships, and correlations within the data.

Feature Engineering: Feature engineering involves selecting, transforming, or creating features from the input data to improve the model's predictive accuracy. This step is crucial for identifying relevant factors that influence the outcome.

Algorithms: Various machine learning algorithms are used in predictive modeling, including linear regression, decision trees, random forests, support vector machines, neural networks, and more. The choice of algorithm depends on the nature of the problem and the dataset.

Model Training: The selected algorithm is trained on the training dataset, where it adjusts its parameters to fit the historical data and minimize prediction errors.

Validation: After training, the model's performance is evaluated using a separate validation dataset. This helps assess how well the model generalizes to new, unseen data.

Hyperparameter Tuning: Fine-tuning the model's hyperparameters, such as learning rates or tree depths, can optimize its performance.

Predictive Output: Once the model is trained and validated, it can be used to make predictions on new, unseen data. These predictions can be in the form of numerical values, classifications, probabilities, or recommendations.

Continuous Learning: Predictive AI models can be designed for continuous learning, where they adapt and improve over time as new data becomes available. This is particularly useful in dynamic environments.

Use Cases: Predictive AI models have a wide range of applications, including fraud detection, customer churn prediction, demand forecasting, disease diagnosis, recommendation systems, and more.

Accuracy and Interpretability: Model accuracy is a critical factor, but interpretability is also important, especially in fields where understanding the reasons behind predictions is crucial (e.g., healthcare or finance).

Deployment: Once a predictive AI model is trained and validated, it can be deployed in production environments to make real-time predictions or recommendations. Deployment may involve integrating the model into software applications or systems.

Predictive AI models are valuable tools for organizations seeking to leverage data-driven insights to optimize processes, make informed decisions, and gain a competitive edge. However, it's essential to consider ethical and privacy implications when working with predictive models, especially when handling sensitive data or making critical predictions.