Predictive or Discriminative AI leverages data by focusing on the differences between categories rather than understanding how the data is generated. Instead of modeling the underlying distribution of each class, these models, such as Support Vector Machines or Neural Networks, analyze labeled training datasets to learn the conditional probability of a label given specific input features. Through an iterative training process, the AI adjusts its internal weights to construct a mathematical "decision boundary" or hyperplane that best separates the data points into distinct groups.
When new, unseen information is introduced, the model does not attempt to recreate the data; instead, it maps the new input features against this pre-established boundary to determine which side of the divide the data falls on, instantly assigning the appropriate category or label based on its position relative to the separator.
| Process Stage | Mechanism | Objective |
|---|---|---|
| 1. Data Ingestion | The model consumes pairs of inputs (features) and outputs (labels) from a training set. | To establish ground truth and identify the relationship between raw data and specific categories. |
| 2. Feature Extraction | The algorithm identifies high-value variables and patterns that distinguish one class from another like edges in an image or keywords in text. | To isolate the specific signals that cause a data point to belong to Class A versus Class B. |
| 3. Boundary Construction | The model uses optimization algorithms (like gradient descent) to define a mathematical line or curve that maximizes the separation between classes. | To create a rigid decision boundary that minimizes the error in distinguishing between existing labels. |
| 4. Inference (New Data) | New, unlabeled data is plotted against the established decision boundary. | To determine the conditional probability of the new data belonging to a specific class. |
| 5. Label Assignment | The system outputs the label corresponding to the side of the boundary where the data point landed. | To provide a definitive categorization (prediction) for the new information. |
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