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[[1]]
           [,1]
-[1,] -5.422091
-[2,]  4.781280
-[3,]  5.289693
-[4,] -5.083941
+[1,] -5.422077
+[2,]  4.781271
+[3,]  5.289690
+[4,] -5.083926
 
 [[2]]
-[1] 2.370601
+[1] 2.370607

Ejercicio: interpreta la red en términos de qué unidades están encendidas (valor cercano a 1) o apagadas (valor cercano a 0). ¿Puedes ajustar este modelo con dos tres unidades intermedias? Haz varias pruebas: ¿qué dificultades encuentras?

@@ -1086,12 +1086,12 @@

Ejemplo (reg preds |> head()
          [,1]
-[1,]  9.117618
-[2,] 22.663441
-[3,]  8.569652
-[4,]  8.558598
-[5,]  8.842549
-[6,]  8.559493
+[1,] 9.117623 +[2,] 22.663464 +[3,] 8.569628 +[4,] 8.558584 +[5,] 8.842519 +[6,] 8.559466

Y obtenemos el siguiente resultado:

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[[1]]
             [,1]
-[1,]  0.34734303
+[1,]  0.34734306
 [2,]  1.01705003
 [3,] -0.05472932
-[4,] -0.02247143
-[5,]  0.51263177
-[6,]  0.55927491
+[4,] -0.02247145
+[5,]  0.51263183
+[6,]  0.55927497
 [7,]  0.45200703
 
 [[2]]
-[1] -0.9558301
+[1] -0.9558302

que coinciden con los valores que obtuvimos usando regresión logística de glm. La única diferencia es que el algoritmo de optimización que se usa en cada caso es diferente: con keras utilizamos descenso en gradiente, mientras que glm usa Newton-Raphson.

diff --git a/09-clasificacion-2.html b/09-clasificacion-2.html index 1fd1d0dd..1171b4eb 100644 --- a/09-clasificacion-2.html +++ b/09-clasificacion-2.html @@ -726,8 +726,8 @@

Ejemplo

3 0.00345 0.00448 1 4 0.00406 0.00897 1 5 0.00437 0.0135 1 - 6 0.00475 0.0224 1 - 7 0.00485 0.0269 1 + 6 0.00476 0.0224 1 + 7 0.00486 0.0269 1 8 0.00504 0.0314 1 9 0.00512 0.0359 1 10 0.00562 0.0404 1 @@ -812,7 +812,7 @@

Ejemplo

modelo .metric .estimator .estimate <chr> <chr> <chr> <dbl> 1 IMC y edad roc_auc binary 0.761 -2 Todas roc_auc binary 0.866 +2 Todas roc_auc binary 0.864

En este ejemplo, vemos que casi no importa qué perfil de especificidad y sensibilidad busquemos: el clasificador que usa todas las variables domina casi siempre al clasificador que sólo utiliza IMC y edad.

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-
- @@ -873,23 +873,23 @@

Ejemplo: dia calibracion_gpos |> gt() |> fmt_number(where(is_double), decimals = 3)

-
- @@ -1446,23 +1446,23 @@

preds_tbl |> head() |> gt() |> fmt_number(where(is_double), decimals = 3)

-
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system.time(modelo <- xgb.train(d_entrena, verbose = 1, nrounds = 10000, params = params))

   user  system elapsed 
- 11.252   0.351   5.954 
+ 9.440 0.265 5.062
xgb.save(model = modelo, fname = "./cache/casas_boost.xgb")
diff --git a/14-interpretacion.html b/14-interpretacion.html index 50cac15b..c3aa93fd 100644 --- a/14-interpretacion.html +++ b/14-interpretacion.html @@ -972,7 +972,7 @@

Ejemplo

} graf_caso(data, contrib, 10, pred_base)
-
[1] "Predicción base: 0.18, Predicción: 0.82"
+
[1] "Predicción base: 0.17, Predicción: 0.87"

@@ -981,7 +981,7 @@

Ejemplo

graf_caso(data, contrib, 102, pred_base)
-
[1] "Predicción base: 0.18, Predicción: 0.85"
+
[1] "Predicción base: 0.17, Predicción: 0.85"

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  • La implementación oficial de R es lm, que en general tiene buen desempeño para datos que caben en memoria:

    @@ -797,7 +797,7 @@

    Ejemplo

    [[1]]
                 [,1]
    -[1,] -0.22045916
    +[1,] -0.22045918
     [2,]  0.23149671
     [3,]  0.09673478
     
    @@ -817,11 +817,11 @@ 

    Ejemplo

    [[1]]
                  [,1]
     [1,] 0.0079817399
    -[2,] 0.0019486139
    +[2,] 0.0019486138
     [3,] 0.0005532746
     
     [[2]]
    -[1] 0.0003491883
    +[1] 0.0003491882
    coef(lm.fit(cbind(1, x_ent), y_ent))
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    get_weights(modelo)

    [[1]]
    -             [,1]
    -[1,] -2.515035629
    -[2,] -0.010236964
    -[3,] -0.007203732
    +            [,1]
    +[1,] -2.64986229
    +[2,] -0.05415167
    +[3,]  0.04267785
     
     [[2]]
    -[1] -0.3029693
    +[1] -0.4793204

    Y verificamos que concuerda con la salida de lm:

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