Update 2_6_Model_Tuning.ipynb

Fixed typos.

ml1/2_6_Model_Tuning.ipynb

@@ -39,7 +39,7 @@
     "* [Train classifier](#Train-classifier)\n",
     "* [More about Pipelines](#More-about-Pipelines)\n",
     "* [Tuning the algorithm](#Tuning-the-algorithm)\n",
-    "\t* [Grid Search for Parameter optimization](#Grid-Search-for-Parameter-optimization)\n",
+    "\t* [Grid Search for Hyperparameter optimization](#Grid-Search-for-Hyperparameter-optimization)\n",
     "* [Evaluating the algorithm](#Evaluating-the-algorithm)\n",
     "\t* [K-Fold validation](#K-Fold-validation)\n",
     "* [References](#References)\n"
@@ -56,9 +56,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous [notebook](2_5_2_Decision_Tree_Model.ipynb), we got an accuracy of 9.47. Could we get a better accuracy if we tune the parameters of the estimator?\n",
+    "In the previous [notebook](2_5_2_Decision_Tree_Model.ipynb), we got an accuracy of 9.47. Could we get a better accuracy if we tune the hyperparameters of the estimator?\n",
     "\n",
-    "The goal of this notebook is to learn how to tune an algorithm by opimizing its parameters using grid search."
+    "The goal of this notebook is to learn how to tune an algorithm by opimizing its hyperparameters using grid search."
    ]
   },
   {
@@ -300,21 +300,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can try different values for these parameters and observe the results."
+    "You can try different values for these hyperparameters and observe the results."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Grid Search for Parameter optimization"
+    "### Grid Search for Hyperparameter optimization"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Changing manually the parameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the parameters as an *optimization problem*. \n",
+    "Changing manually the hyperparameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the parameters as an *optimization problem*. \n",
     "\n",
     "The sklearn comes with several optimization techniques for this purpose, such as  **grid search** and  **randomized search**. In this notebook we are going to introduce the former one."
    ]
@@ -405,7 +405,7 @@
    "source": [
     "We have got an *improvement* from 0.947 to 0.953 with k-fold.\n",
     "\n",
-    "We are now to try to fit the best combination of the parameters of the algorithm. It can take some time to compute it."
+    "We are now to try to fit the best combination of the hyperparameters of the algorithm. It can take some time to compute it."
    ]
   },
   {
@@ -414,12 +414,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Set the parameters by cross-validation\n",
+    "# Set the hyperparameters by cross-validation\n",
     "\n",
     "from sklearn.metrics import classification_report, recall_score, precision_score, make_scorer\n",
     "\n",
-    "# set of parameters to test\n",
-    "tuned_parameters = [{'max_depth': np.arange(3, 10),\n",
+    "# set of hyperparameters to test\n",
+    "tuned_hyperparameters = [{'max_depth': np.arange(3, 10),\n",
     "#                     'max_weights': [1, 10, 100, 1000]},\n",
     "                     'criterion': ['gini', 'entropy'], \n",
     "                     'splitter': ['best', 'random'],\n",
@@ -431,7 +431,7 @@
     "scores = ['precision', 'recall']\n",
     "\n",
     "for score in scores:\n",
-    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
+    "    print(\"# Tuning hyper-hyperparameters for %s\" % score)\n",
     "    print()\n",
     "\n",
     "    if score == 'precision':\n",
@@ -440,10 +440,10 @@
     "        scorer = make_scorer(recall_score, average='weighted', zero_division=0)\n",
     "    \n",
     "    # cv = the fold of the cross-validation cv, defaulted to 5\n",
-    "    gs = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=10, scoring=scorer)\n",
+    "    gs = GridSearchCV(DecisionTreeClassifier(), tuned_hyperparameters, cv=10, scoring=scorer)\n",
     "    gs.fit(x_train, y_train)\n",
     "\n",
-    "    print(\"Best parameters set found on development set:\")\n",
+    "    print(\"Best hyperparameters set found on development set:\")\n",
     "    print()\n",
     "    print(gs.best_params_)\n",
     "    print()\n",
@@ -520,7 +520,7 @@
     "* [Plot the decision surface of a decision tree on the iris dataset](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html)\n",
     "* [scikit-learn : Machine Learning Simplified](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
     "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019.\n",
-    "* [Parameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
+    "* [Hyperparameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
     "* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)"
    ]
   },