fix deep lgbm comments

Author: ndharasz

example_model.ipynb

@@ -134,7 +134,7 @@
         ")\n",
         "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
         "# model = lgb.LGBMRegressor(\n",
-        "#     n_estimators=2 30_000,\n",
+        "#     n_estimators=30_000,\n",
         "#     learning_rate=0.001,\n",
         "#     max_depth=10,\n",
         "#     num_leaves=2**10,\n",

feature_neutralization.ipynb

@@ -1995,7 +1995,7 @@
         ")\n",
         "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
         "# model = lgb.LGBMRegressor(\n",
-        "#     n_estimators=2 30_000,\n",
+        "#     n_estimators=30_000,\n",
         "#     learning_rate=0.001,\n",
         "#     max_depth=10,\n",
         "#     num_leaves=2**10,\n",

hello_numerai.ipynb

@@ -37,8 +37,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.9/58.9 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.4/34.4 MB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
@@ -89,8 +89,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Available versions:\n",
             " ['v5.0']\n",
@@ -1851,7 +1851,7 @@
         ")\n",
         "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
         "# model = lgb.LGBMRegressor(\n",
-        "#     n_estimators=2 30_000,\n",
+        "#     n_estimators=30_000,\n",
         "#     learning_rate=0.001,\n",
         "#     max_depth=10,\n",
         "#     num_leaves=2**10,\n",

target_ensemble.ipynb

@@ -1932,7 +1932,7 @@
         "    )\n",
         "    # We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
         "    # model = lgb.LGBMRegressor(\n",
-        "    #     n_estimators=2 30_000,\n",
+        "    #     n_estimators=30_000,\n",
         "    #     learning_rate=0.001,\n",
         "    #     max_depth=10,\n",
         "    #     num_leaves=2**10,\n",