[ci skip] FIX correction for some typos (INRIA#779)

Co-authored-by: ArturoAmorQ <[email protected]> 00379f8
kalona · May 23, 2024 · 482baaa · 482baaa
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commit 482baaa
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diff --git a/.buildinfo b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 4d0b8ff6c57249ded5f0eb4d309710a9
+config: b1d24180e61c8890f3448461bef0fb15
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/...9d5a5eb639acd189d15b05717687f6dd43d71.png → ...a0ee80c0eb2b53a61e0f08645b8560599520f.png b/...9d5a5eb639acd189d15b05717687f6dd43d71.png → ...a0ee80c0eb2b53a61e0f08645b8560599520f.png
diff --git a/_images/ffb89894b012967a35a3f5e92cb58dae3def86d6971ee08cceb9da28fb98840d.png b/_images/ffb89894b012967a35a3f5e92cb58dae3def86d6971ee08cceb9da28fb98840d.png
diff --git a/_images/nested_cross_validation_diagram.png b/_images/nested_cross_validation_diagram.png
diff --git a/_sources/index.md b/_sources/index.md
@@ -36,8 +36,8 @@ interpreting their predictions.
       <a href="https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn">
         "Machine learning in Python with scikit-learn MOOC"
       </a>,
-      is available starting on October 18, 2022 and will last for 3 months. Enroll for
-      the full MOOC experience (quizz solutions, executable notebooks, discussion
+      is available starting on November 8th, 2023 and will remain open in self-paced mode.
+      Enroll for the full MOOC experience (quiz solutions, executable notebooks, discussion
       forum, etc ...) !
       </br>
       The MOOC is free and the platform does not use the student data for any other purpose
@@ -79,7 +79,7 @@ You can cite us through the project's Zenodo archive using the following DOI:
 [10.5281/zenodo.7220306](https://doi.org/10.5281/zenodo.7220306).
 
 The following repository includes the notebooks, exercises and solutions to the
-exercises (but not the quizz solutions ;):
+exercises (but not the quizzes' solutions ;):
 
   https://github.com/INRIA/scikit-learn-mooc/
 

diff --git a/_sources/python_scripts/01_tabular_data_exploration.py b/_sources/python_scripts/01_tabular_data_exploration.py
@@ -70,6 +70,15 @@
 # %%
 adult_census.head()
 
+# %% [markdown]
+# An alternative is to omit the `head` method. This would output the intial and
+# final rows and columns, but everything in between is not shown by default. It
+# also provides the dataframe's dimensions at the bottom in the format `n_rows`
+# x `n_columns`.
+
+# %%
+adult_census
+
 # %% [markdown]
 # The column named **class** is our target variable (i.e., the variable which we
 # want to predict). The two possible classes are `<=50K` (low-revenue) and

diff --git a/_sources/python_scripts/02_numerical_pipeline_hands_on.py b/_sources/python_scripts/02_numerical_pipeline_hands_on.py
@@ -34,7 +34,7 @@
 adult_census = pd.read_csv("../datasets/adult-census.csv")
 # drop the duplicated column `"education-num"` as stated in the first notebook
 adult_census = adult_census.drop(columns="education-num")
-adult_census.head()
+adult_census
 
 # %% [markdown]
 # The next step separates the target from the data. We performed the same
@@ -44,7 +44,7 @@
 data, target = adult_census.drop(columns="class"), adult_census["class"]
 
 # %%
-data.head()
+data
 
 # %%
 target
@@ -95,7 +95,7 @@
 # the `object` data type.
 
 # %%
-data.head()
+data
 
 # %% [markdown]
 # We see that the `object` data type corresponds to columns containing strings.
@@ -105,7 +105,7 @@
 
 # %%
 numerical_columns = ["age", "capital-gain", "capital-loss", "hours-per-week"]
-data[numerical_columns].head()
+data[numerical_columns]
 
 # %% [markdown]
 # Now that we limited the dataset to numerical columns only, we can analyse

diff --git a/_sources/python_scripts/02_numerical_pipeline_introduction.py b/_sources/python_scripts/02_numerical_pipeline_introduction.py
@@ -39,7 +39,7 @@
 # Let's have a look at the first records of this dataframe:
 
 # %%
-adult_census.head()
+adult_census
 
 # %% [markdown]
 # We see that this CSV file contains all information: the target that we would
@@ -56,10 +56,10 @@
 
 # %%
 data = adult_census.drop(columns=[target_name])
-data.head()
+data
 
 # %% [markdown]
-# We can now linger on the variables, also denominated features, that we later
+# We can now focus on the variables, also denominated features, that we later
 # use to build our predictive model. In addition, we can also check how many
 # samples are available in our dataset.
 

diff --git a/_sources/python_scripts/03_categorical_pipeline.py b/_sources/python_scripts/03_categorical_pipeline.py
@@ -81,7 +81,7 @@
 
 # %%
 data_categorical = data[categorical_columns]
-data_categorical.head()
+data_categorical
 
 # %%
 print(f"The dataset is composed of {data_categorical.shape[1]} features")
@@ -194,7 +194,7 @@
 
 # %%
 print(f"The dataset is composed of {data_categorical.shape[1]} features")
-data_categorical.head()
+data_categorical
 
 # %%
 data_encoded = encoder.fit_transform(data_categorical)
@@ -253,7 +253,7 @@
 # and check the generalization performance of this machine learning pipeline using
 # cross-validation.
 #
-# Before we create the pipeline, we have to linger on the `native-country`.
+# Before we create the pipeline, we have to focus on the `native-country`.
 # Let's recall some statistics regarding this column.
 
 # %%
@@ -329,9 +329,10 @@
 print(f"The accuracy is: {scores.mean():.3f} ± {scores.std():.3f}")
 
 # %% [markdown]
-# As you can see, this representation of the categorical variables is
-# slightly more predictive of the revenue than the numerical variables
-# that we used previously.
+# As you can see, this representation of the categorical variables is slightly
+# more predictive of the revenue than the numerical variables that we used
+# previously. The reason being that we have more (predictive) categorical
+# features than numerical ones.
 
 # %% [markdown]
 #

diff --git a/_sources/python_scripts/03_categorical_pipeline_column_transformer.py b/_sources/python_scripts/03_categorical_pipeline_column_transformer.py
@@ -165,7 +165,7 @@
 # method. As an example, we predict on the five first samples from the test set.
 
 # %%
-data_test.head()
+data_test
 
 # %%
 model.predict(data_test)[:5]

diff --git a/_sources/python_scripts/cross_validation_ex_01.py b/_sources/python_scripts/cross_validation_ex_01.py
@@ -45,7 +45,7 @@
 # exercise.
 #
 # Also, this classifier can become more flexible/expressive by using a so-called
-# kernel that makes the model become non-linear. Again, no requirement regarding
+# kernel that makes the model become non-linear. Again, no undestanding regarding
 # the mathematics is required to accomplish this exercise.
 #
 # We will use an RBF kernel where a parameter `gamma` allows to tune the

diff --git a/_sources/python_scripts/cross_validation_grouping.py b/_sources/python_scripts/cross_validation_grouping.py
@@ -7,9 +7,8 @@
 
 # %% [markdown]
 # # Sample grouping
-# We are going to linger into the concept of sample groups. As in the previous
-# section, we will give an example to highlight some surprising results. This
-# time, we will use the handwritten digits dataset.
+# In this notebook we present the concept of **sample groups**. We use the
+# handwritten digits dataset to highlight some surprising results.
 
 # %%
 from sklearn.datasets import load_digits
@@ -18,8 +17,17 @@
 data, target = digits.data, digits.target
 
 # %% [markdown]
-# We will recreate the same model used in the previous notebook: a logistic
-# regression classifier with a preprocessor to scale the data.
+# We create a model consisting of a logistic regression classifier with a
+# preprocessor to scale the data.
+#
+# ```{note}
+# Here we use a `MinMaxScaler` as we know that each pixel's gray-scale is
+# strictly bounded between 0 (white) and 16 (black). This makes `MinMaxScaler`
+# more suited in this case than `StandardScaler`, as some pixels consistently
+# have low variance (pixels at the borders might almost always be zero if most
+# digits are centered in the image). Then, using `StandardScaler` can result in
+# a very high scaled value due to division by a small number.
+# ```
 
 # %%
 from sklearn.preprocessing import MinMaxScaler
@@ -29,8 +37,10 @@
 model = make_pipeline(MinMaxScaler(), LogisticRegression(max_iter=1_000))
 
 # %% [markdown]
-# We will use the same baseline model. We will use a `KFold` cross-validation
-# without shuffling the data at first.
+# The idea is to compare the estimated generalization performance using
+# different cross-validation techniques and see how such estimations are
+# impacted by underlying data structures. We first use a `KFold`
+# cross-validation without shuffling the data.
 
 # %%
 from sklearn.model_selection import cross_val_score, KFold
@@ -59,9 +69,9 @@
 )
 
 # %% [markdown]
-# We observe that shuffling the data improves the mean accuracy. We could go a
-# little further and plot the distribution of the testing score. We can first
-# concatenate the test scores.
+# We observe that shuffling the data improves the mean accuracy. We can go a
+# little further and plot the distribution of the testing score. For such
+# purpose we concatenate the test scores.
 
 # %%
 import pandas as pd
@@ -72,29 +82,29 @@
 ).T
 
 # %% [markdown]
-# Let's plot the distribution now.
+# Let's now plot the score distributions.
 
 # %%
 import matplotlib.pyplot as plt
 
-all_scores.plot.hist(bins=10, edgecolor="black", alpha=0.7)
+all_scores.plot.hist(bins=16, edgecolor="black", alpha=0.7)
 plt.xlim([0.8, 1.0])
 plt.xlabel("Accuracy score")
 plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
 _ = plt.title("Distribution of the test scores")
 
 # %% [markdown]
-# The cross-validation testing error that uses the shuffling has less variance
-# than the one that does not impose any shuffling. It means that some specific
-# fold leads to a low score in this case.
+# Shuffling the data results in a higher cross-validated test accuracy with less
+# variance compared to when the data is not shuffled. It means that some
+# specific fold leads to a low score in this case.
 
 # %%
 print(test_score_no_shuffling)
 
 # %% [markdown]
-# Thus, there is an underlying structure in the data that shuffling will break
-# and get better results. To get a better understanding, we should read the
-# documentation shipped with the dataset.
+# Thus, shuffling the data breaks the underlying structure and thus makes the
+# classification task easier to our model. To get a better understanding, we can
+# read the dataset description in more detail:
 
 # %%
 print(digits.DESCR)
@@ -165,7 +175,7 @@
     groups[lb:up] = group_id
 
 # %% [markdown]
-# We can check the grouping by plotting the indices linked to writer ids.
+# We can check the grouping by plotting the indices linked to writers' ids.
 
 # %%
 plt.plot(groups)
@@ -176,8 +186,9 @@
 _ = plt.title("Underlying writer groups existing in the target")
 
 # %% [markdown]
-# Once we group the digits by writer, we can use cross-validation to take this
-# information into account: the class containing `Group` should be used.
+# Once we group the digits by writer, we can incorporate this information into
+# the cross-validation process by using group-aware variations of the strategies
+# we have explored in this course, for example, the `GroupKFold` strategy.
 
 # %%
 from sklearn.model_selection import GroupKFold
@@ -191,10 +202,12 @@
 )
 
 # %% [markdown]
-# We see that this strategy is less optimistic regarding the model
-# generalization performance. However, this is the most reliable if our goal is
-# to make handwritten digits recognition writers independent. Besides, we can as
-# well see that the standard deviation was reduced.
+# We see that this strategy leads to a lower generalization performance than the
+# other two techniques. However, this is the most reliable estimate if our goal
+# is to evaluate the capabilities of the model to generalize to new unseen
+# writers. In this sense, shuffling the dataset (or alternatively using the
+# writers' ids as a new feature) would lead the model to memorize the different
+# writer's particular handwriting.
 
 # %%
 all_scores = pd.DataFrame(
@@ -207,13 +220,18 @@
 ).T
 
 # %%
-all_scores.plot.hist(bins=10, edgecolor="black", alpha=0.7)
+all_scores.plot.hist(bins=16, edgecolor="black", alpha=0.7)
 plt.xlim([0.8, 1.0])
 plt.xlabel("Accuracy score")
 plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
 _ = plt.title("Distribution of the test scores")
 
 # %% [markdown]
-# As a conclusion, it is really important to take any sample grouping pattern
-# into account when evaluating a model. Otherwise, the results obtained will be
-# over-optimistic in regards with reality.
+# In conclusion, accounting for any sample grouping patterns is crucial when
+# assessing a model’s ability to generalize to new groups. Without this
+# consideration, the results may appear overly optimistic compared to the actual
+# performance.
+#
+# The interested reader can learn about other group-aware cross-validation
+# techniques in the [scikit-learn user
+# guide](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data).
diff --git a/_sources/python_scripts/cross_validation_learning_curve.py b/_sources/python_scripts/cross_validation_learning_curve.py
@@ -13,7 +13,7 @@
 # generalizing. Besides these aspects, it is also important to understand how
 # the different errors are influenced by the number of samples available.
 #
-# In this notebook, we will show this aspect by looking a the variability of
+# In this notebook, we will show this aspect by looking at the variability of
 # the different errors.
 #
 # Let's first load the data and create the same model as in the previous

diff --git a/_sources/python_scripts/cross_validation_sol_01.py b/_sources/python_scripts/cross_validation_sol_01.py
@@ -39,7 +39,7 @@
 # exercise.
 #
 # Also, this classifier can become more flexible/expressive by using a so-called
-# kernel that makes the model become non-linear. Again, no requirement regarding
+# kernel that makes the model become non-linear. Again, no understanding regarding
 # the mathematics is required to accomplish this exercise.
 #
 # We will use an RBF kernel where a parameter `gamma` allows to tune the