daviddoji/ISLP_labs
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fix refs again (#76)

Browse Source 
* Ch2->Ch02

* fixed latex refs again, somehow crept back in

* fixed the page refs, formats synced

* unsynced

* executed notebook besides 10

* warnings for lasso

* allow saving of output in notebooks

* Ch10 executed
This commit is contained in:
Jonathan Taylor
2026-02-04 17:40:52 -08:00
committed by GitHub
parent 3d9af7c4b0
commit 6bf6160a3d
25 changed files with 21872 additions and 3191 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
Ch04-classification-lab.Rmd
View File

@@ -394,7 +394,7 @@ lda.fit(X_train, L_train)
```
Here we have used the list comprehensions introduced
in Section~\ref{Ch3-linreg-lab:multivariate-goodness-of-fit}. Looking at our first line above, we see that the right-hand side is a list
in Section 3.6.4. Looking at our first line above, we see that the right-hand side is a list
of length two. This is because the code `for M in [X_train, X_test]` iterates over a list
of length two. While here we loop over a list,
the list comprehension method works when looping over any iterable object.
@@ -443,7 +443,7 @@ lda.scalings_
```
These values provide the linear combination of `Lag1` and `Lag2` that are used to form the LDA decision rule. In other words, these are the multipliers of the elements of $X=x$ in (\ref{Ch4:bayes.multi}).
These values provide the linear combination of `Lag1` and `Lag2` that are used to form the LDA decision rule. In other words, these are the multipliers of the elements of $X=x$ in (4.24).
If $-0.64\times `Lag1` - 0.51 \times `Lag2` $ is large, then the LDA classifier will predict a market increase, and if it is small, then the LDA classifier will predict a market decline.
```{python}
@@ -452,7 +452,7 @@ lda_pred = lda.predict(X_test)
```
As we observed in our comparison of classification methods
(Section~\ref{Ch4:comparison.sec}), the LDA and logistic
(Section 4.5), the LDA and logistic
regression predictions are almost identical.
```{python}
@@ -511,7 +511,7 @@ The LDA classifier above is the first classifier from the
`sklearn` library. We will use several other objects
from this library. The objects
follow a common structure that simplifies tasks such as cross-validation,
which we will see in Chapter~\ref{Ch5:resample}. Specifically,
which we will see in Chapter 5. Specifically,
the methods first create a generic classifier without
referring to any data. This classifier is then fit
to data with the `fit()` method and predictions are
@@ -797,7 +797,7 @@ feature_std.std()
```
Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page~\pageref{Ch4-varformula}.
Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 183.
In this case it does not matter, as long as the variables are all on the same scale.
Using the function `train_test_split()` we now split the observations into a test set,
@@ -864,7 +864,7 @@ This is double the rate that one would obtain from random guessing.
The number of neighbors in KNN is referred to as a *tuning parameter*, also referred to as a *hyperparameter*.
We do not know *a priori* what value to use. It is therefore of interest
to see how the classifier performs on test data as we vary these
parameters. This can be achieved with a `for` loop, described in Section~\ref{Ch2-statlearn-lab:for-loops}.
parameters. This can be achieved with a `for` loop, described in Section 2.3.8.
Here we use a for loop to look at the accuracy of our classifier in the group predicted to purchase
insurance as we vary the number of neighbors from 1 to 5:
@@ -891,7 +891,7 @@ As a comparison, we can also fit a logistic regression model to the
data. This can also be done
with `sklearn`, though by default it fits
something like the *ridge regression* version
of logistic regression, which we introduce in Chapter~\ref{Ch6:varselect}. This can
of logistic regression, which we introduce in Chapter 6. This can
be modified by appropriately setting the argument `C` below. Its default
value is 1 but by setting it to a very large number, the algorithm converges to the same solution as the usual (unregularized)
logistic regression estimator discussed above.
@@ -935,7 +935,7 @@ confusion_table(logit_labels, y_test)
```
## Linear and Poisson Regression on the Bikeshare Data
Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section~\ref{Ch4:sec:pois}.
Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section 4.6.
The response `bikers` measures the number of bike rentals per hour
in Washington, DC in the period 2010--2012.
@@ -976,7 +976,7 @@ variables constant, there are on average about 7 more riders in
February than in January. Similarly there are about 16.5 more riders
in March than in January.
The results seen in Section~\ref{sec:bikeshare.linear}
The results seen in Section 4.6.1
used a slightly different coding of the variables `hr` and `mnth`, as follows:
```{python}
@@ -1030,7 +1030,7 @@ np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)
```
To reproduce the left-hand side of Figure~\ref{Ch4:bikeshare}
To reproduce the left-hand side of Figure 4.13
we must first obtain the coefficient estimates associated with
`mnth`. The coefficients for January through November can be obtained
directly from the `M2_lm` object. The coefficient for December
@@ -1070,7 +1070,7 @@ ax_month.set_ylabel('Coefficient', fontsize=20);
```
Reproducing the right-hand plot in Figure~\ref{Ch4:bikeshare} follows a similar process.
Reproducing the right-hand plot in Figure 4.13 follows a similar process.
```{python}
coef_hr = S2[S2.index.str.contains('hr')]['coef']
@@ -1105,7 +1105,7 @@ M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()
```
We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure~\ref{Ch4:bikeshare.pois}. We first complete these coefficients as before.
We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before.
```{python}
S_pois = summarize(M_pois)