Overview
The model inference process is a critical phase in machine learning where trained models are utilized to make predictions on new data. This process involves preparing the data, running the model, and evaluating the results to ensure accuracy. Understanding how to effectively manage inference can sig...
Key Terms
Example: The model's inference on new data resulted in accurate predictions.
Example: The model training involved using historical sales data.
Example: Data normalization helped improve model performance.
Example: The confusion matrix showed the number of true positives and false negatives.
Example: High precision indicates that most positive predictions were correct.
Example: High recall means the model identified most of the actual positives.
Related Topics
Model Training Techniques
Explore various methods for training machine learning models effectively.
intermediateData Preprocessing Methods
Learn about techniques to prepare data for machine learning applications.
intermediateReal-Time Machine Learning
Understand how to implement machine learning models in real-time systems.
advancedKey Concepts
PredictionModel EvaluationData InputOutput Generation