7+ Resume Power Verbs to Replace "Supported"

another word for supported on resume

7+ Resume Power Verbs to Replace "Supported"

When describing contributions to a team or project on a resume, using varied and impactful language is crucial. Instead of relying on the common term “supported,” consider stronger action verbs that highlight specific accomplishments. For instance, instead of stating “Supported the marketing team,” one might write “Facilitated marketing campaigns” or “Contributed to marketing strategy development.” This demonstrates a more active role and provides a clearer picture of the candidate’s involvement.

Choosing precise verbs strengthens a resume by showcasing quantifiable achievements and demonstrating the impact of one’s work. This level of detail helps potential employers understand the value a candidate brings and differentiates them from other applicants. Historically, resumes have evolved from simple lists of previous employment to dynamic documents highlighting skills and accomplishments. This shift necessitates a more nuanced vocabulary, moving beyond generic terms like “supported” to showcase a candidate’s unique contributions.

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Fix: 0d/1d Target Tensor Expected, Multi-Target Not Supported Error

0d or 1d target tensor expected multi-target not supported

Fix: 0d/1d Target Tensor Expected, Multi-Target Not Supported Error

This error typically arises within machine learning frameworks when the shape of the target variable (the data the model is trying to predict) is incompatible with the model’s expected input. Models often anticipate a target variable represented as a single column of values (1-dimensional) or a single value per sample (0-dimensional). Providing a target with multiple columns or dimensions (multi-target) signifies a problem in data preparation or model configuration, leading to this error message. For instance, a model designed to predict a single numerical value (like price) cannot directly handle multiple target values (like price, location, and condition) simultaneously.

Correctly shaping the target variable is fundamental for successful model training. This ensures compatibility between the data and the algorithm’s internal workings, preventing errors and allowing for efficient learning. The expected target shape usually reflects the specific task a model is designed to perform. Regression models frequently require 1-dimensional or 0-dimensional targets, while some specialized models might handle multi-dimensional targets for tasks like multi-label classification. Historical development of machine learning libraries has increasingly emphasized clear error messages to guide users in resolving data inconsistencies.

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