This commit is contained in:
Morten Hjorth-Jensen
2025-09-22 06:48:27 +02:00
parent a7b6575e84
commit ae5d5e08ec
14 changed files with 5348 additions and 129 deletions
+1 -2
View File
@@ -808,7 +808,7 @@ i = <span style="color: #B452CD">0</span>
plt.figure()
plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = <span style="color: #CD5555">&#39;cross_val_score&#39;</span>)
plt.plot(np.log10(lambdas), estimated_mse_KFold, <span style="color: #CD5555">&#39;r--&#39;</span>, label = <span style="color: #CD5555">&#39;KFold&#39;</span>)
<span style="color: #228B22">#plt.plot(np.log10(lambdas), estimated_mse_KFold, &#39;r--&#39;, label = &#39;KFold&#39;)</span>
plt.xlabel(<span style="color: #CD5555">&#39;log10(lambda)&#39;</span>)
plt.ylabel(<span style="color: #CD5555">&#39;mse&#39;</span>)
@@ -1192,7 +1192,6 @@ the probability of a given category. This leads us to the logistic function.
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> mean_squared_error
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">IPython.display</span> <span style="color: #8B008B; font-weight: bold">import</span> display
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">pylab</span> <span style="color: #8B008B; font-weight: bold">import</span> plt, mpl
plt.style.use(<span style="color: #CD5555">&#39;seaborn&#39;</span>)
mpl.rcParams[<span style="color: #CD5555">&#39;font.family&#39;</span>] = <span style="color: #CD5555">&#39;serif&#39;</span>
<span style="color: #228B22"># Where to save the figures and data files</span>
+1 -2
View File
@@ -816,7 +816,7 @@ i = <span style="color: #B452CD">0</span>
plt.figure()
plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = <span style="color: #CD5555">&#39;cross_val_score&#39;</span>)
plt.plot(np.log10(lambdas), estimated_mse_KFold, <span style="color: #CD5555">&#39;r--&#39;</span>, label = <span style="color: #CD5555">&#39;KFold&#39;</span>)
<span style="color: #228B22">#plt.plot(np.log10(lambdas), estimated_mse_KFold, &#39;r--&#39;, label = &#39;KFold&#39;)</span>
plt.xlabel(<span style="color: #CD5555">&#39;log10(lambda)&#39;</span>)
plt.ylabel(<span style="color: #CD5555">&#39;mse&#39;</span>)
@@ -1190,7 +1190,6 @@ the probability of a given category. This leads us to the logistic function.
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> mean_squared_error
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">IPython.display</span> <span style="color: #8B008B; font-weight: bold">import</span> display
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">pylab</span> <span style="color: #8B008B; font-weight: bold">import</span> plt, mpl
plt.style.use(<span style="color: #CD5555">&#39;seaborn&#39;</span>)
mpl.rcParams[<span style="color: #CD5555">&#39;font.family&#39;</span>] = <span style="color: #CD5555">&#39;serif&#39;</span>
<span style="color: #228B22"># Where to save the figures and data files</span>
+1 -2
View File
@@ -893,7 +893,7 @@ i <span style="color: #666666">=</span> <span style="color: #666666">0</span>
plt<span style="color: #666666">.</span>figure()
plt<span style="color: #666666">.</span>plot(np<span style="color: #666666">.</span>log10(lambdas), estimated_mse_sklearn, label <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;cross_val_score&#39;</span>)
plt<span style="color: #666666">.</span>plot(np<span style="color: #666666">.</span>log10(lambdas), estimated_mse_KFold, <span style="color: #BA2121">&#39;r--&#39;</span>, label <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;KFold&#39;</span>)
<span style="color: #408080; font-style: italic">#plt.plot(np.log10(lambdas), estimated_mse_KFold, &#39;r--&#39;, label = &#39;KFold&#39;)</span>
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;log10(lambda)&#39;</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;mse&#39;</span>)
@@ -1267,7 +1267,6 @@ the probability of a given category. This leads us to the logistic function.
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> mean_squared_error
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">IPython.display</span> <span style="color: #008000; font-weight: bold">import</span> display
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">pylab</span> <span style="color: #008000; font-weight: bold">import</span> plt, mpl
plt<span style="color: #666666">.</span>style<span style="color: #666666">.</span>use(<span style="color: #BA2121">&#39;seaborn&#39;</span>)
mpl<span style="color: #666666">.</span>rcParams[<span style="color: #BA2121">&#39;font.family&#39;</span>] <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;serif&#39;</span>
<span style="color: #408080; font-style: italic"># Where to save the figures and data files</span>
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+120 -121
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@@ -2,7 +2,7 @@
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@@ -14,7 +14,7 @@
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@@ -27,7 +27,7 @@
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@@ -45,7 +45,7 @@
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@@ -64,7 +64,7 @@
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@@ -81,7 +81,7 @@
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@@ -103,7 +103,7 @@
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@@ -113,7 +113,7 @@
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@@ -140,7 +140,7 @@
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@@ -166,7 +166,7 @@
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@@ -183,7 +183,7 @@
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@@ -203,7 +203,7 @@
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@@ -232,7 +232,7 @@
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@@ -255,7 +255,7 @@
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@@ -273,7 +273,7 @@
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@@ -285,7 +285,7 @@
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@@ -302,7 +302,7 @@
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@@ -314,7 +314,7 @@
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@@ -324,7 +324,7 @@
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@@ -336,7 +336,7 @@
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@@ -353,7 +353,7 @@
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@@ -365,7 +365,7 @@
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@@ -375,7 +375,7 @@
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@@ -387,7 +387,7 @@
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@@ -397,7 +397,7 @@
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@@ -409,7 +409,7 @@
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@@ -421,7 +421,7 @@
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@@ -437,7 +437,7 @@
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@@ -448,7 +448,7 @@
{
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@@ -507,7 +507,7 @@
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@@ -545,7 +545,7 @@
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@@ -572,7 +572,7 @@
{
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@@ -634,7 +634,7 @@
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@@ -659,7 +659,7 @@
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@@ -687,7 +687,7 @@
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@@ -700,7 +700,7 @@
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@@ -788,7 +788,7 @@
"plt.figure()\n",
"\n",
"plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
"plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n",
"#plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n",
"\n",
"plt.xlabel('log10(lambda)')\n",
"plt.ylabel('mse')\n",
@@ -800,7 +800,7 @@
},
{
"cell_type": "markdown",
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"id": "1cab90d5",
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@@ -811,7 +811,7 @@
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@@ -900,7 +900,7 @@
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@@ -910,7 +910,7 @@
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@@ -923,7 +923,7 @@
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@@ -1001,7 +1001,7 @@
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@@ -1023,7 +1023,7 @@
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@@ -1049,7 +1049,7 @@
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@@ -1073,7 +1073,7 @@
},
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@@ -1098,7 +1098,7 @@
},
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@@ -1110,7 +1110,7 @@
},
{
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@@ -1128,7 +1128,7 @@
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{
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@@ -1146,7 +1146,7 @@
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@@ -1157,7 +1157,7 @@
},
{
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@@ -1184,7 +1184,7 @@
},
{
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@@ -1197,7 +1197,7 @@
{
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"metadata": {
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"editable": true
@@ -1215,7 +1215,6 @@
"from sklearn.metrics import mean_squared_error\n",
"from IPython.display import display\n",
"from pylab import plt, mpl\n",
"plt.style.use('seaborn')\n",
"mpl.rcParams['font.family'] = 'serif'\n",
"\n",
"# Where to save the figures and data files\n",
@@ -1262,7 +1261,7 @@
},
{
"cell_type": "markdown",
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"id": "a51c0a93",
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@@ -1275,7 +1274,7 @@
{
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@@ -1294,7 +1293,7 @@
},
{
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@@ -1305,7 +1304,7 @@
},
{
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@@ -1317,7 +1316,7 @@
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@@ -1336,7 +1335,7 @@
},
{
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@@ -1358,7 +1357,7 @@
},
{
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@@ -1370,7 +1369,7 @@
},
{
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@@ -1380,7 +1379,7 @@
},
{
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@@ -1393,7 +1392,7 @@
{
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@@ -1458,7 +1457,7 @@
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{
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@@ -1470,7 +1469,7 @@
},
{
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@@ -1485,7 +1484,7 @@
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@@ -1497,7 +1496,7 @@
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@@ -1509,7 +1508,7 @@
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@@ -1526,7 +1525,7 @@
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@@ -1540,7 +1539,7 @@
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@@ -1550,7 +1549,7 @@
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@@ -1562,7 +1561,7 @@
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@@ -1574,7 +1573,7 @@
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@@ -1586,7 +1585,7 @@
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@@ -1597,7 +1596,7 @@
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@@ -1609,7 +1608,7 @@
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@@ -1620,7 +1619,7 @@
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@@ -1636,7 +1635,7 @@
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@@ -1648,7 +1647,7 @@
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@@ -1658,7 +1657,7 @@
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@@ -1670,7 +1669,7 @@
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@@ -1685,7 +1684,7 @@
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@@ -1697,7 +1696,7 @@
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@@ -1708,7 +1707,7 @@
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{
"cell_type": "markdown",
"id": "c13c8084",
"id": "22ea395e",
"metadata": {
"editable": true
},
@@ -1720,7 +1719,7 @@
},
{
"cell_type": "markdown",
"id": "28c175f9",
"id": "2e9061e6",
"metadata": {
"editable": true
},
@@ -1732,7 +1731,7 @@
},
{
"cell_type": "markdown",
"id": "822f9d8f",
"id": "5478dfcf",
"metadata": {
"editable": true
},
@@ -1744,7 +1743,7 @@
},
{
"cell_type": "markdown",
"id": "b7ef7713",
"id": "ee3b3f11",
"metadata": {
"editable": true
},
@@ -1754,7 +1753,7 @@
},
{
"cell_type": "markdown",
"id": "e0d677b0",
"id": "ba4abaf7",
"metadata": {
"editable": true
},
@@ -1766,7 +1765,7 @@
},
{
"cell_type": "markdown",
"id": "1bd3a1e4",
"id": "49034035",
"metadata": {
"editable": true
},
@@ -1780,7 +1779,7 @@
},
{
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"id": "2229abec",
"id": "ac971cb1",
"metadata": {
"editable": true
},
@@ -1792,7 +1791,7 @@
},
{
"cell_type": "markdown",
"id": "20185c58",
"id": "05fd7d42",
"metadata": {
"editable": true
},
@@ -1802,7 +1801,7 @@
},
{
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"id": "9bca0545",
"id": "035035dc",
"metadata": {
"editable": true
},
@@ -1814,7 +1813,7 @@
},
{
"cell_type": "markdown",
"id": "b7a80eb4",
"id": "790836d0",
"metadata": {
"editable": true
},
@@ -1824,7 +1823,7 @@
},
{
"cell_type": "markdown",
"id": "d9d417b0",
"id": "3be49378",
"metadata": {
"editable": true
},
@@ -1836,7 +1835,7 @@
},
{
"cell_type": "markdown",
"id": "3405d5d0",
"id": "af2acbd8",
"metadata": {
"editable": true
},
@@ -1847,7 +1846,7 @@
},
{
"cell_type": "markdown",
"id": "b1fe4b02",
"id": "1ff433e0",
"metadata": {
"editable": true
},
@@ -1870,7 +1869,7 @@
},
{
"cell_type": "markdown",
"id": "fab2b45c",
"id": "7b40d96f",
"metadata": {
"editable": true
},
@@ -1882,7 +1881,7 @@
},
{
"cell_type": "markdown",
"id": "599b6e62",
"id": "bb9dcd5e",
"metadata": {
"editable": true
},
@@ -1892,7 +1891,7 @@
},
{
"cell_type": "markdown",
"id": "2a90fb4f",
"id": "a88f89b8",
"metadata": {
"editable": true
},
@@ -1904,7 +1903,7 @@
},
{
"cell_type": "markdown",
"id": "64b0f466",
"id": "3b06d53a",
"metadata": {
"editable": true
},
@@ -1921,7 +1920,7 @@
},
{
"cell_type": "markdown",
"id": "e82c6a01",
"id": "fdbc41f1",
"metadata": {
"editable": true
},
@@ -1940,7 +1939,7 @@
},
{
"cell_type": "markdown",
"id": "3da5eca8",
"id": "beac8e5d",
"metadata": {
"editable": true
},
@@ -1957,7 +1956,7 @@
},
{
"cell_type": "markdown",
"id": "62626aea",
"id": "e1bb0759",
"metadata": {
"editable": true
},
@@ -1972,7 +1971,7 @@
},
{
"cell_type": "markdown",
"id": "b54b2119",
"id": "c981e68b",
"metadata": {
"editable": true
},
@@ -1982,7 +1981,7 @@
},
{
"cell_type": "markdown",
"id": "cc6c3444",
"id": "bcc80f6a",
"metadata": {
"editable": true
},
@@ -1998,7 +1997,7 @@
},
{
"cell_type": "markdown",
"id": "fd89c356",
"id": "494e2809",
"metadata": {
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},
@@ -2010,7 +2009,7 @@
},
{
"cell_type": "markdown",
"id": "dc825324",
"id": "bec907c8",
"metadata": {
"editable": true
},
@@ -2021,7 +2020,7 @@
},
{
"cell_type": "markdown",
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"id": "d9e91ef8",
"metadata": {
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},
@@ -2033,7 +2032,7 @@
},
{
"cell_type": "markdown",
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"id": "b9edd237",
"metadata": {
"editable": true
},
@@ -2043,7 +2042,7 @@
},
{
"cell_type": "markdown",
"id": "0c251c1b",
"id": "5af4086e",
"metadata": {
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},
@@ -2057,7 +2056,7 @@
},
{
"cell_type": "markdown",
"id": "1284b5bd",
"id": "00d037d1",
"metadata": {
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},
@@ -2069,7 +2068,7 @@
},
{
"cell_type": "markdown",
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"id": "dc1a264e",
"metadata": {
"editable": true
},
@@ -2079,7 +2078,7 @@
},
{
"cell_type": "markdown",
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"id": "b65899bd",
"metadata": {
"editable": true
},
@@ -2091,7 +2090,7 @@
},
{
"cell_type": "markdown",
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"id": "5951fc7a",
"metadata": {
"editable": true
},
@@ -2103,7 +2102,7 @@
},
{
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"id": "6e3eb5ff",
"metadata": {
"editable": true
},
@@ -2116,7 +2115,7 @@
{
"cell_type": "code",
"execution_count": 9,
"id": "e226ab3b",
"id": "e1b62460",
"metadata": {
"collapsed": false,
"editable": true
@@ -2259,7 +2258,7 @@
},
{
"cell_type": "markdown",
"id": "0981d00d",
"id": "bde30fac",
"metadata": {
"editable": true
},
@@ -2278,7 +2277,7 @@
{
"cell_type": "code",
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"id": "297a1581",
"id": "9fb3602e",
"metadata": {
"collapsed": false,
"editable": true
@@ -2321,7 +2320,7 @@
},
{
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"id": "b12732fb",
"id": "9d92d000",
"metadata": {
"editable": true
},
@@ -2335,7 +2334,7 @@
{
"cell_type": "code",
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"id": "6bf79dfe",
"id": "fa95e885",
"metadata": {
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"editable": true
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@@ -527,7 +527,7 @@ for lmb in lambdas:
plt.figure()
plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')
#plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')
plt.xlabel('log10(lambda)')
plt.ylabel('mse')
@@ -845,7 +845,6 @@ from sklearn.utils import resample
from sklearn.metrics import mean_squared_error
from IPython.display import display
from pylab import plt, mpl
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
# Where to save the figures and data files