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cd14f9ebcb
* split up select.md * array-join.md basic refactoring * distinct.md basic refactoring * format.md basic refactoring * from.md basic refactoring * group-by.md basic refactoring * having.md basic refactoring * additional index.md refactoring * into-outfile.md basic refactoring * join.md basic refactoring * limit.md basic refactoring * limit-by.md basic refactoring * order-by.md basic refactoring * prewhere.md basic refactoring * adjust operators/index.md links * adjust sample.md links * adjust more links * adjust operatots links * fix some links * adjust aggregate function article titles * basic refactor of remaining select clauses * absolute paths in make_links.sh * run make_links.sh * remove old select.md locations * translate docs/es * translate docs/fr * translate docs/fa * remove old operators.md location * change operators.md links * adjust links in docs/es * adjust links in docs/es * minor texts adjustments * wip * update machine translations to use new links * fix changelog * es build fixes * get rid of some select.md links * temporary adjust ru links * temporary adjust more ru links * improve curly brace handling * adjust ru as well * fa build fix * ru link fixes * zh link fixes * temporary disable part of anchor checks
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64 | Funciones de aprendizaje automático |
Funciones de aprendizaje automático
evalMLMethod (predicción)
Predicción utilizando modelos de regresión ajustados utiliza evalMLMethod
función. Ver enlace en linearRegression
.
Regresión lineal estocástica
El stochasticLinearRegression la función agregada implementa el método de descenso de gradiente estocástico utilizando el modelo lineal y la función de pérdida MSE. Utilizar evalMLMethod
para predecir sobre nuevos datos.
Regresión logística estocástica
El stochasticLogisticRegression la función de agregado implementa el método de descenso de gradiente estocástico para el problema de clasificación binaria. Utilizar evalMLMethod
para predecir sobre nuevos datos.