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fixing whitespace in Rmd so diff of errata is cleaner (#46)

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* fixing whitespace in Rmd so diff of errata is cleaner

* reapply kwargs fix
This commit is contained in:
Jonathan Taylor
2025-04-03 12:25:17 -07:00
committed by GitHub
parent f132c18a1c
commit 6d7e40588b
12 changed files with 392 additions and 410 deletions
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1
Ch03-linreg-lab.Rmd
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# Linear Regression # Linear Regression
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch03-linreg-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch03-linreg-lab.ipynb">

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Ch04-classification-lab.Rmd
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# Logistic Regression, LDA, QDA, and KNN # Logistic Regression, LDA, QDA, and KNN
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch04-classification-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch04-classification-lab.ipynb">

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Ch05-resample-lab.Rmd
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# Cross-Validation and the Bootstrap # Cross-Validation and the Bootstrap
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb">

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Ch06-varselect-lab.Rmd
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# Linear Models and Regularization Methods # Linear Models and Regularization Methods
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch06-varselect-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch06-varselect-lab.ipynb">

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Ch08-baggboost-lab.Rmd
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# Tree-Based Methods # Tree-Based Methods
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch08-baggboost-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch08-baggboost-lab.ipynb">

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Ch09-svm-lab.Rmd
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# Support Vector Machines # Support Vector Machines
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch09-svm-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch09-svm-lab.ipynb">

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Ch10-deeplearning-lab.Rmd
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@@ -1392,7 +1392,7 @@ We’ll now make a plot to compare our neural network results with the
lasso. lasso.
```{python} ```{python}
%%capture # %%capture
fig, axes = subplots(1, 2, figsize=(16, 8), sharey=True) fig, axes = subplots(1, 2, figsize=(16, 8), sharey=True)
for ((X_, Y_), for ((X_, Y_),
data_, data_,

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Ch11-surv-lab.Rmd
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# Survival Analysis # Survival Analysis
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch11-surv-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch11-surv-lab.ipynb">

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Ch12-unsup-lab.Rmd
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@@ -212,7 +212,7 @@ We can plot the PVE explained by each component, as well as the cumulative PVE.
plot the proportion of variance explained. plot the proportion of variance explained.
```{python} ```{python}
%%capture # %%capture
fig, axes = plt.subplots(1, 2, figsize=(15, 6)) fig, axes = plt.subplots(1, 2, figsize=(15, 6))
ticks = np.arange(pcaUS.n_components_)+1 ticks = np.arange(pcaUS.n_components_)+1
ax = axes[0] ax = axes[0]