MTN Made the distinction between predictor and transformer clearer

notebooks/02_numerical_pipeline_introduction.ipynb

@@ -162,11 +162,18 @@
     "![Predictor fit diagram](../figures/api_diagram-predictor.fit.svg)\n",
     "\n",
     "In scikit-learn an object that has a `fit` method is called an **estimator**.\n",
+    "If the estimator additionally has :\n",
+    "- a  `predict` method, it is called a **predictor**. Examples of predictors\n",
+    "  are classifiers or regressors.\n",
+    "- a `transform` method, it is called a **transformer**. Examples of\n",
+    "  transformers are scalers or encoders. We will see more about transformers in\n",
+    "  the next notebook.\n",
+    "\n",
     "The method `fit` is composed of two elements: (i) a **learning algorithm** and\n",
     "(ii) some **model states**. The learning algorithm takes the training data and\n",
     "training target as input and sets the model states. These model states are\n",
-    "later used to either predict (for classifiers and regressors) or transform\n",
-    "data (for transformers).\n",
+    "later used to either predict or transform data as explained above. See the\n",
+    "glossary for more detailed definitions.\n",
     "\n",
     "Both the learning algorithm and the type of model states are specific to each\n",
     "type of model."
@@ -204,8 +211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "An estimator (an object with a `fit` method) with a `predict` method is called\n",
-    "a **predictor**. We can illustrate the prediction mechanism as follows:\n",
+    "We can illustrate the prediction mechanism as follows:\n",
     "\n",
     "![Predictor predict diagram](../figures/api_diagram-predictor.predict.svg)\n",
     "\n",

notebooks/02_numerical_pipeline_scaling.ipynb

@@ -137,6 +137,7 @@
     "We show how to apply such normalization using a scikit-learn transformer\n",
     "called `StandardScaler`. This transformer shifts and scales each feature\n",
     "individually so that they all have a 0-mean and a unit standard deviation.\n",
+    "We recall that transformers are estimators that have a `transform` method.\n",
     "\n",
     "We now investigate different steps used in scikit-learn to achieve such a\n",
     "transformation of the data.\n",

python_scripts/02_numerical_pipeline_introduction.py

@@ -101,11 +101,18 @@
 # ![Predictor fit diagram](../figures/api_diagram-predictor.fit.svg)
 #
 # In scikit-learn an object that has a `fit` method is called an **estimator**.
+# If the estimator additionally has :
+# - a  `predict` method, it is called a **predictor**. Examples of predictors
+#   are classifiers or regressors.
+# - a `transform` method, it is called a **transformer**. Examples of
+#   transformers are scalers or encoders. We will see more about transformers in
+#   the next notebook.
+#
 # The method `fit` is composed of two elements: (i) a **learning algorithm** and
 # (ii) some **model states**. The learning algorithm takes the training data and
 # training target as input and sets the model states. These model states are
-# later used to either predict (for classifiers and regressors) or transform
-# data (for transformers).
+# later used to either predict or transform data as explained above. See the
+# glossary for more detailed definitions.
 #
 # Both the learning algorithm and the type of model states are specific to each
 # type of model.
@@ -124,8 +131,7 @@
 target_predicted = model.predict(data)
 
 # %% [markdown]
-# An estimator (an object with a `fit` method) with a `predict` method is called
-# a **predictor**. We can illustrate the prediction mechanism as follows:
+# We can illustrate the prediction mechanism as follows:
 #
 # ![Predictor predict diagram](../figures/api_diagram-predictor.predict.svg)
 #

python_scripts/02_numerical_pipeline_scaling.py

@@ -88,6 +88,7 @@
 # We show how to apply such normalization using a scikit-learn transformer
 # called `StandardScaler`. This transformer shifts and scales each feature
 # individually so that they all have a 0-mean and a unit standard deviation.
+# We recall that transformers are estimators that have a `transform` method.
 #
 # We now investigate different steps used in scikit-learn to achieve such a
 # transformation of the data.