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Reduce description length for SVC multilingual support #747

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cristian-tamblay merged 1 commit into from
Jul 6, 2026
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Reduce description length for SVC multilingual support #747

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51 changes: 5 additions & 46 deletions DashAI/back/models/scikit_learn/svc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,52 +254,11 @@ class SVC(TabularClassificationModel, SklearnLikeClassifier, _SVC):
de="Support-Vektor-Maschine (SVM)",
)
DESCRIPTION: str = MultilingualString(
en=(
"Support Vector Machine (SVM) is a supervised machine learning algorithm "
"used for classification and regression tasks. It works by finding the "
"optimal hyperplane that maximizes the margin between different classes "
"in a high dimensional feature space. SVMs are effective in cases where "
"the number of features is large relative to the number of samples and "
"can model complex, nonlinear decision boundaries through the use of "
"kernel functions such as linear, polynomial, and radial basis function "
"(RBF) kernels."
),
es=(
"La Máquina de Vectores de Soporte (SVM) es un algoritmo de aprendizaje "
"automático supervisado utilizado para tareas de clasificación y "
"regresión. Funciona encontrando el hiperplano óptimo que maximiza el "
"margen entre las distintas clases en un espacio de características de "
"alta dimensionalidad. Las SVM son especialmente efectivas cuando el "
"número de características es grande en relación con el número de "
"muestras y pueden modelar fronteras de decisión complejas y no lineales "
"mediante el uso de funciones kernel como lineal, polinomial y de base "
"radial (RBF)."
),
pt=(
"A Máquina de Vetores de Suporte (SVM) é um algoritmo de aprendizado "
"de máquina supervisionado utilizado para tarefas de classificação e "
"regressão. Funciona encontrando o hiperplano ótimo que maximiza a "
"margem entre as diferentes classes em um espaço de características de "
"alta dimensionalidade. As SVMs são especialmente eficazes quando o "
"número de características é grande em relação ao número de amostras e "
"podem modelar fronteiras de decisão complexas e não lineares mediante "
"o uso de funções kernel como linear, polinomial e de base radial (RBF)."
),
zh=(
"支持向量机（SVM）是一种监督学习算法，通过在高维特征空间中"
"寻找最优超平面来最大化类间间隔，支持线性、多项式和径向基函数（RBF）核。"
),
de=(
"Die Support-Vektor-Maschine (SVM) ist ein überwachter "
"Machine Learning Algorithmus für Klassifikations- und "
"Regressionsaufgaben. Sie findet die optimale Hyperebene, die die "
"Margin zwischen verschiedenen Klassen in einem hochdimensionalen "
"Merkmalsraum maximiert. SVMs sind besonders effektiv, wenn die Anzahl "
"der Merkmale im Verhältnis zur Anzahl der Stichproben groß ist, und "
"können komplexe, nichtlineare Entscheidungsgrenzen durch den Einsatz "
"von Kernelfunktionen wie linear, polynomial und radialer Basisfunktion "
"(RBF) modellieren."
),
en="Finds the optimal hyperplane that maximises the margin between classes.",
es="Encuentra el hiperplano óptimo que maximiza el margen entre clases.",
pt="Encontra o hiperplano ótimo que maximiza a margem entre classes.",
zh="寻找最优超平面以最大化类间间隔的分类算法。",
de="Findet die optimale Hyperebene, die den Margin zwischen Klassen maximiert.",
)
COLOR: str = "#FF80AB"
ICON: str = "Timeline"
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