Training

Definition

Training is the process of feeding a machine learning model a large dataset, allowing the algorithm to adjust its internal parameters until it can accurately map the input data to the desired output. This is the "learning" part of machine learning.

Why It Matters

Training is the most computationally intensive phase of machine learning. The quality and quantity of the training data largely determine how well the final model will perform.

Contextual Example

To train a spam filter, you would feed it a training set of thousands of emails, each labeled as "spam" or "not spam." The model would analyze the words and patterns in these emails and learn to associate certain features with spam.

Common Misunderstandings

• Training is an iterative process. The model makes a prediction, compares it to the correct answer, calculates the "error," and adjusts itself to reduce that error on the next iteration.
• Once a model is trained, it can be used for "inference" to make predictions on new, unseen data.

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