Reach For AI - Recent entries in Data & Model Concepts

Big Data

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Big Data – Large, complex datasets that require advanced analytics techniques.

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Training Data – Data used to teach an AI model how to recognize patterns.

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Test Data – Data used to evaluate the performance of an AI model.

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Overfitting – When an ML model learns noise instead of patterns, performing well on training data but poorly on new data.

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Underfitting – When an ML model is too simple to capture the underlying patterns in data.

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Bias – Systematic errors in AI models that lead to unfair or inaccurate predictions.

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Variance – A model’s sensitivity to fluctuations in training data, leading to overfitting.

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Feature Engineering – The process of selecting and transforming variables to improve model performance.

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Dimensionality Reduction – Techniques like PCA that reduce the number of input variables in a model.

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Embeddings – A representation of data (e.g., words or images) in a lower-dimensional space to capture relationships.