tuning parameter
[ˈtjuːnɪŋ ˈpærəmɪtər]
nounpl: tuning parameters
parâmetro de ajuste
1. A variable or coefficient in a machine learning or statistical model that is set before training begins and controls aspects of the learning process, such as model complexity or regularization strength
The learning rate is a critical tuning parameter that affects how quickly the neural network converges.
A taxa de aprendizado é um parâmetro de ajuste crítico que afeta a rapidez com que a rede neural converge.
2. A hyperparameter that must be optimized to improve model performance and prevent overfitting or underfitting
We performed cross-validation to find the optimal tuning parameters for our regression model.
Realizamos validação cruzada para encontrar os parâmetros de ajuste ideais para nosso modelo de regressão.
3. In regularization techniques, a parameter that controls the trade-off between model fit and model complexity
In ridge regression, the tuning parameter lambda controls the penalty applied to large coefficients.
Na regressão ridge, o parâmetro de ajuste lambda controla a penalidade aplicada aos coeficientes grandes.
This is primarily technical terminology used in data science, machine learning, and statistical modeling communities. The term is widely used in both English-speaking and Brazilian Portuguese-speaking academic and professional environments. In Brazil, 'parâmetro de ajuste' and 'hiperparâmetro' are both accepted, with usage depending on context and precision requirements.
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