Jupytext sync (#49)
* fixes to Rmd from Daniela's errata * synced the notebooks * syncing notebooks * all but Chapter10 * chapter 10
This commit is contained in:
Jonathan Taylor
GitHub
@@ -1,3 +1,16 @@
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---
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jupyter:
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jupytext:
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cell_metadata_filter: -all
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formats: ipynb,Rmd
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main_language: python
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text_representation:
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extension: .Rmd
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format_name: rmarkdown
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format_version: '1.2'
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jupytext_version: 1.16.7
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---
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# Introduction to Python
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<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch02-statlearn-lab.ipynb">
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@@ -70,7 +83,7 @@ print('fit a model with', 11, 'variables')
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The following command will provide information about the `print()` function.
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```{python}
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print?
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# print?
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```
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@@ -220,7 +233,7 @@ documentation associated with the function `fun`, if it exists.
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We can try this for `np.array()`.
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```{python}
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np.array?
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# np.array?
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```
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This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`.
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@@ -1,3 +1,16 @@
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---
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jupyter:
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jupytext:
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cell_metadata_filter: -all
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formats: ipynb,Rmd
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main_language: python
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text_representation:
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extension: .Rmd
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format_name: rmarkdown
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format_version: '1.2'
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jupytext_version: 1.16.7
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---
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# Linear Regression
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<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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---
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jupyter:
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jupytext:
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cell_metadata_filter: -all
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formats: ipynb,Rmd
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main_language: python
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text_representation:
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extension: .Rmd
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format_name: rmarkdown
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format_version: '1.2'
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jupytext_version: 1.16.7
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---
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# Logistic Regression, LDA, QDA, and KNN
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<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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---
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jupyter:
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jupytext:
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cell_metadata_filter: -all
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formats: ipynb,Rmd
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main_language: python
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text_representation:
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extension: .Rmd
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format_name: rmarkdown
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format_version: '1.2'
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jupytext_version: 1.16.7
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---
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# Cross-Validation and the Bootstrap
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<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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@@ -207,7 +201,7 @@
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"\n",
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"We can also estimate the validation error for\n",
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"higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n",
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"as a training and test set and returns the MSE on the test set."
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"as training and test sets and returns the MSE on the test set."
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"and `score()` methods, an\n",
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"array of features `X` and a response `Y`. \n",
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"We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n",
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"$K$ results in $K$-fold cross-validation. We have provided a value \n",
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"$k$ results in $k$-fold cross-validation. We have provided a value \n",
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"corresponding to the total number of observations, which results in\n",
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"leave-one-out cross-validation (LOOCV). The `cross_validate()` function produces a dictionary with several components;\n",
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"we simply want the cross-validated test score here (MSE), which is estimated to be 24.23."
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@@ -542,8 +536,8 @@
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"In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n",
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"to the above (and is significantly faster). Here we use `KFold()` to partition the data into $K=10$ random groups. We use `random_state` to set a random seed and initialize a vector `cv_error` in which we will store the CV errors corresponding to the\n",
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"In the CV example above, we used $k=n$, but of course we can also use $k<n$. The code is very similar\n",
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"to the above (and is significantly faster). Here we use `KFold()` to partition the data into $k=10$ random groups. We use `random_state` to set a random seed and initialize a vector `cv_error` in which we will store the CV errors corresponding to the\n",
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"polynomial fits of degrees one to five."
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]
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"Notice that the computation time is much shorter than that of LOOCV.\n",
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"(In principle, the computation time for LOOCV for a least squares\n",
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"linear model should be faster than for $K$-fold CV, due to the\n",
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"linear model should be faster than for $k$-fold CV, due to the\n",
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"availability of the formula~(\\ref{Ch5:eq:LOOCVform}) for LOOCV;\n",
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"however, the generic `cross_validate()` function does not make\n",
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"use of this formula.) We still see little evidence that using cubic\n",
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@@ -608,8 +602,9 @@
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"The `cross_validate()` function is flexible and can take\n",
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"different splitting mechanisms as an argument. For instance, one can use the `ShuffleSplit()` funtion to implement\n",
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"the validation set approach just as easily as K-fold cross-validation."
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"different splitting mechanisms as an argument. For instance, one can use the `ShuffleSplit()`\n",
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"function to implement\n",
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"the validation set approach just as easily as $k$-fold cross-validation."
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||||
}
|
||||
},
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||||
"outputs": [
|
||||
@@ -736,10 +731,10 @@
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||||
"id": "a4b6d9b3",
|
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"metadata": {
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@@ -770,10 +765,10 @@
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}
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||||
},
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"outputs": [
|
||||
@@ -809,10 +804,10 @@
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"id": "64fe1cb6",
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"metadata": {
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"execution": {
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"lines_to_next_cell": 2
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@@ -852,10 +847,10 @@
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||||
"id": "dd16bbae",
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"metadata": {
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"iopub.execute_input": "2025-04-03T19:33:25.194520Z",
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@@ -897,10 +892,10 @@
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||||
"id": "b42b4585",
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"metadata": {
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"execution": {
|
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"shell.execute_reply": "2024-06-04T23:19:15.631370Z"
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"shell.execute_reply": "2025-04-03T19:33:25.394181Z"
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}
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||||
},
|
||||
"outputs": [
|
||||
@@ -963,10 +958,10 @@
|
||||
"id": "6bc11784",
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||||
"metadata": {
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"execution": {
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||||
"iopub.execute_input": "2024-06-04T23:19:15.632802Z",
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"shell.execute_reply": "2024-06-04T23:19:15.634222Z"
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"iopub.execute_input": "2025-04-03T19:33:25.396011Z",
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"shell.execute_reply": "2025-04-03T19:33:25.397676Z"
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"lines_to_next_cell": 0
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@@ -998,10 +993,10 @@
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"id": "740cd50c",
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"metadata": {
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"execution": {
|
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"iopub.execute_input": "2025-04-03T19:33:25.399076Z",
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@@ -1031,10 +1026,10 @@
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"id": "ffb3ec50",
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"metadata": {
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|
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"iopub.execute_input": "2025-04-03T19:33:25.401753Z",
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},
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||||
"lines_to_next_cell": 0
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},
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@@ -1082,10 +1077,10 @@
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"id": "7d561f70",
|
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"metadata": {
|
||||
"execution": {
|
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"shell.execute_reply": "2024-06-04T23:19:17.204614Z"
|
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"iopub.execute_input": "2025-04-03T19:33:25.423860Z",
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"shell.execute_reply": "2025-04-03T19:33:27.088869Z"
|
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},
|
||||
"lines_to_next_cell": 2
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},
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@@ -1132,10 +1127,10 @@
|
||||
"id": "3888aa0a",
|
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"metadata": {
|
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"execution": {
|
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"shell.execute_reply": "2024-06-04T23:19:17.221444Z"
|
||||
"iopub.execute_input": "2025-04-03T19:33:27.090386Z",
|
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"shell.execute_reply": "2025-04-03T19:33:27.106554Z"
|
||||
},
|
||||
"lines_to_next_cell": 2
|
||||
},
|
||||
@@ -1177,7 +1172,7 @@
|
||||
"rely on certain assumptions. For example,\n",
|
||||
"they depend on the unknown parameter $\\sigma^2$, the noise\n",
|
||||
"variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
|
||||
"formula for the standard errors do not rely on the linear model being\n",
|
||||
"formulas for the standard errors do not rely on the linear model being\n",
|
||||
"correct, the estimate for $\\sigma^2$ does. We see\n",
|
||||
" {in Figure~\\ref{Ch3:polyplot} on page~\\pageref{Ch3:polyplot}} that there is\n",
|
||||
"a non-linear relationship in the data, and so the residuals from a\n",
|
||||
@@ -1204,10 +1199,10 @@
|
||||
"id": "acc3e32c",
|
||||
"metadata": {
|
||||
"execution": {
|
||||
"iopub.execute_input": "2024-06-04T23:19:17.222887Z",
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"shell.execute_reply": "2024-06-04T23:19:19.351317Z"
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"iopub.execute_input": "2025-04-03T19:33:27.108124Z",
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"iopub.status.idle": "2025-04-03T19:33:29.434320Z",
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"shell.execute_reply": "2025-04-03T19:33:29.434045Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
@@ -1247,10 +1242,10 @@
|
||||
"id": "dca5340c",
|
||||
"metadata": {
|
||||
"execution": {
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"iopub.execute_input": "2024-06-04T23:19:19.352904Z",
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"iopub.status.busy": "2024-06-04T23:19:19.352827Z",
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"iopub.status.idle": "2024-06-04T23:19:19.360147Z",
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"iopub.execute_input": "2025-04-03T19:33:29.435689Z",
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"iopub.status.busy": "2025-04-03T19:33:29.435602Z",
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"iopub.status.idle": "2025-04-03T19:33:29.444726Z",
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"shell.execute_reply": "2025-04-03T19:33:29.444464Z"
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||||
},
|
||||
"lines_to_next_cell": 0
|
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},
|
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@@ -1288,13 +1283,8 @@
|
||||
"metadata": {
|
||||
"jupytext": {
|
||||
"cell_metadata_filter": "-all",
|
||||
"main_language": "python",
|
||||
"notebook_metadata_filter": "-all"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
"formats": "ipynb,Rmd",
|
||||
"main_language": "python"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -1306,7 +1296,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.3"
|
||||
"version": "3.11.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# 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">
|
||||
|
||||
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@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# Non-Linear Modeling
|
||||
|
||||
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch07-nonlin-lab.ipynb">
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# 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">
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# 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">
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# Deep Learning
|
||||
|
||||
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch10-deeplearning-lab.ipynb">
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# Survival Analysis
|
||||
|
||||
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch11-surv-lab.ipynb">
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# Unsupervised Learning
|
||||
|
||||
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch12-unsup-lab.ipynb">
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1,3 +1,16 @@
|
||||
---
|
||||
jupyter:
|
||||
jupytext:
|
||||
cell_metadata_filter: -all
|
||||
formats: ipynb,Rmd
|
||||
main_language: python
|
||||
text_representation:
|
||||
extension: .Rmd
|
||||
format_name: rmarkdown
|
||||
format_version: '1.2'
|
||||
jupytext_version: 1.16.7
|
||||
---
|
||||
|
||||
# Multiple Testing
|
||||
|
||||
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch13-multiple-lab.ipynb">
|
||||
|
||||
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user