minor change

python/iia/crop_production.ipynb

@@ -235,10 +235,10 @@
     "\n",
     "* Model: neural network with one hidden layer using the tanh activation function (see [scikit-learn Multi-layer Perceptron](https://scikit-learn.org/stable/modules/neural_networks_supervised.html))\n",
     "* Cost function: quadratic loss (or mean squared error) with momentum regularization\n",
+    "* Training procedure: [leave-one-out](https://scikit-learn.org/stable/modules/cross_validation.html#leave-one-out-loo) cross validation (one example used for test, a subset (10% random examples) used for validation and the rest for training) with [early stopping](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_early_stopping.html)\n",
     "* Optimization algorithm: stochastic gradient descent (fixed learning rate) or Quasi-Newton BFGS (see scikit-learn Multi-layer Perceptron [algorithms](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#algorithms))  \n",
     "* Data pre-processing: mean normalization \n",
-    "* Training procedure: [leave-one-out](https://scikit-learn.org/stable/modules/cross_validation.html#leave-one-out-loo) cross validation (one example used for test, a subset (10% random examples) used for validation and the rest for training) with [early stopping](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_early_stopping.html)\n",
-    "* [Ensemble method](https://scikit-learn.org/stable/modules/ensemble.html): neural network model with different weights initialization and validation set \n",
+    "* [Ensemble method](https://scikit-learn.org/stable/modules/ensemble.html): neural network models with different weights initialization and validation set (bootstrap replicates: sampling with replacement from the training dataset after the test set has been selected)\n",
     "\n",
     "\n",
     "We create an MLP with one hidden layer to approximate a function that represents the yeld of a crop (maize or millet) depending on the yearly values of precipitation, temperature, number of hours with temperatures above 30°C, amount of nitrogen fertilizer, and amount of manure fertilizer. We use a 4-5-1 architecture. We can compute the preactivation of the hidden layer by a matrix multiplications between the units of the input layer and the units of the hidden layer.\n",
@@ -274,7 +274,7 @@
    "metadata": {},
    "source": [
     "## Ensemble method\n",
-    "The methodology can be described algorithmically. The same procedure is used for the two ensembles, one for maize model (4 input units, 5 hidden, 1 output) and one for millet model (5 input units, 5 hidden, 1 output)\n"
+    "The methodology can be described algorithmically. The same procedure is used for the two ensembles, one for maize model (4 input units, 5 hidden, 1 output) and one for millet model (5 input units, 5 hidden, 1 output). The construction of the predictors from the bootstrap samples can be done in parallel on a multicore CPU.\n"
    ]
   },
   {