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Node consistency in machine learning, particularly in constraint satisfaction problems (CSPs), refers to a property of consistency that focuses on individual variables (nodes) and their domains. In CSPs, a node consistency check ensures that the values in the domain of a single variable are consistent with the unary constraints associated with that variable. 

Unary Constraints: Unary constraints are constraints that involve a single variable. These constraints specify restrictions or conditions on the values that a variable can take without considering interactions with other variables. 

Node Consistency: A CSP is considered node consistent if, for each variable, the values in its domain satisfy the unary constraints associated with that variable. In other words, the values allowed for each variable must conform to the individual restrictions imposed by its unary constraints. 

Enforcement: Achieving node consistency often involves enforcing the unary constraints on each variable. This enforcement process can eliminate inconsistent values from the domains of variables. 

Ensuring node consistency is an initial step in the process of solving CSPs. It simplifies the problem and can reduce the search space by eliminating values that are inconsistent with individual variable constraints. Techniques such as constraint propagation may be employed to enforce node consistency during the solution process. 

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