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MTN Exercise M4.01 target correction #871

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Merged
ArturoAmorQ merged 3 commits into from
Jan 19, 2026
Merged

MTN Exercise M4.01 target correction #871

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b59fb0b
fix series to dataframe
Jan 14, 2026
d268203
FIX Pin jupyter-book version (#873)
ArturoAmorQ Jan 15, 2026
b06233a
Update notebooks
Jan 19, 2026
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2 changes: 1 addition & 1 deletion environment-dev.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -13,4 +13,4 @@ dependencies:
- packaging
- pip
- pip:
- jupyter-book >= 0.11
- jupyter-book < 2.0
2 changes: 1 addition & 1 deletion notebooks/linear_models_ex_01.ipynb
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Expand Up @@ -43,7 +43,7 @@
"penguins = pd.read_csv(\"../datasets/penguins_regression.csv\")\n",
"feature_name = \"Flipper Length (mm)\"\n",
"target_name = \"Body Mass (g)\"\n",
"data, target = penguins[[feature_name]], penguins[target_name]"
"data, target = penguins[[feature_name]], penguins[[target_name]]"
]
},
{
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4 changes: 2 additions & 2 deletions notebooks/linear_models_sol_01.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@
"penguins = pd.read_csv(\"../datasets/penguins_regression.csv\")\n",
"feature_name = \"Flipper Length (mm)\"\n",
"target_name = \"Body Mass (g)\"\n",
"data, target = penguins[[feature_name]], penguins[target_name]"
"data, target = penguins[[feature_name]], penguins[[target_name]]"
]
},
{
Expand Down Expand Up @@ -152,7 +152,7 @@
"def goodness_fit_measure(true_values, predictions):\n",
" # we compute the error between the true values and the predictions of our\n",
" # model\n",
" errors = np.ravel(true_values) - np.ravel(predictions)\n",
" errors = true_values - predictions\n",
" # We have several possible strategies to reduce all errors to a single value.\n",
" # Computing the mean error (sum divided by the number of element) might seem\n",
" # like a good solution. However, we have negative errors that will misleadingly\n",
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2 changes: 1 addition & 1 deletion python_scripts/linear_models_ex_01.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@
penguins = pd.read_csv("../datasets/penguins_regression.csv")
feature_name = "Flipper Length (mm)"
target_name = "Body Mass (g)"
data, target = penguins[[feature_name]], penguins[target_name]
data, target = penguins[[feature_name]], penguins[[target_name]]

# %% [markdown]
# ### Model definition
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4 changes: 2 additions & 2 deletions python_scripts/linear_models_sol_01.py
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Expand Up @@ -34,7 +34,7 @@
penguins = pd.read_csv("../datasets/penguins_regression.csv")
feature_name = "Flipper Length (mm)"
target_name = "Body Mass (g)"
data, target = penguins[[feature_name]], penguins[target_name]
data, target = penguins[[feature_name]], penguins[[target_name]]

# %% [markdown]
# ### Model definition
Expand Down Expand Up @@ -106,7 +106,7 @@ def linear_model_flipper_mass(
def goodness_fit_measure(true_values, predictions):
# we compute the error between the true values and the predictions of our
# model
errors = np.ravel(true_values) - np.ravel(predictions)
errors = true_values - predictions
# We have several possible strategies to reduce all errors to a single value.
# Computing the mean error (sum divided by the number of element) might seem
# like a good solution. However, we have negative errors that will misleadingly
Expand Down
2 changes: 1 addition & 1 deletion requirements-dev.txt
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Expand Up @@ -3,7 +3,7 @@ pandas >= 1
matplotlib
seaborn >= 0.13
plotly
jupyter-book>=0.11
jupyter-book < 2.0
jupytext
beautifulsoup4
IPython