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6 results for “features”

Formulae (6)

z = \frac{x - \mu}{\sigma}

Standardizes features to zero mean and unit variance.

x' = \frac{x - x_{min}}{x_{max} - x_{min}}

Rescales features to the range [0, 1].

\hat{y} = \mathbf{w}^\top \mathbf{x} + b

Predicts a continuous value as a weighted sum of input features plus a bias.

P(y\mid \mathbf{x}) \propto P(y)\prod_i P(x_i\mid y)

Classifies by assuming features are conditionally independent given the class.

\text{tfidf} = tf \cdot \log\dfrac{N}{df}

Weights a word by its frequency in a document against its rarity across the corpus.

\hat{x}=\dfrac{x-\mu}{\sqrt{\sigma^2+\epsilon}}\,\gamma+\beta

Normalizes across features within a single sample; standard in Transformers.