In machine learning, particularly in the context of neural networks and some other algorithms, a "weight" refers to a parameter used by the model to adjust the contribution of a feature or an input to the final output of the model.

For instance, in a neural network, each connection between neurons has a weight associated with it. These weights are adjusted during the training process, enabling the model to learn from the data and make accurate predictions. The values of these weights determine the strength and direction of the influence that a particular input has on the model's prediction.

During the training phase, the model adjusts these weights iteratively using optimization algorithms (such as Gradient Descent) to minimize the difference between the predicted output and the actual output. This process involves updating the weights in a way that reduces the error or loss function, effectively fine-tuning the model to improve its performance on the training data.

In summary, weights in a machine learning model are parameters that the model adjusts based on the training data to give different levels of importance to different features, enabling the model to learn and make accurate predictions or classifications. Adjusting these weights is a critical aspect of the model learning process.