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Fix typos and improve clarity in markdown cells

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This commit is contained in:
Carlos A. Iglesias
2026-04-09 11:51:47 +02:00
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parent b83bcf5c2b
commit 77ed6c91be
1 changed files with 10 additions and 10 deletions
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ml3/2_4_1_Exercise.ipynb
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@@ -197,7 +197,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The features are simply the position of each point in the 2 dimension plane.\n", "The features are simply the position of each point in the 2-dimensional plane.\n",
"\n", "\n",
"In other words, a point $\\mathbf{x}$ is represented by its values $x_1$ and $x_2$:\n", "In other words, a point $\\mathbf{x}$ is represented by its values $x_1$ and $x_2$:\n",
"\n", "\n",
@@ -208,14 +208,14 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Perform the classification task on several classifiers" "## Perform the classification task on several classifiers."
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Following, the classification on the spiral is done with several classifiers. We can see the performance on each class (each spiral), and their decision surfaces." Following the classification on the spiral is done with several classifiers. We can see the performance on each class (each spiral), and their decision surfaces."
] ]
}, },
{ {
@@ -266,7 +266,7 @@
"source": [ "source": [
"from sklearn.linear_model import LogisticRegression\n", "from sklearn.linear_model import LogisticRegression\n",
"\n", "\n",
"lr = LogisticRegression(n_jobs=-1)\n", "lr = LogisticRegression()\n",
"lr.fit(X,y)\n", "lr.fit(X,y)\n",
"\n", "\n",
"lr_preds = lr.predict(X_test)\n", "lr_preds = lr.predict(X_test)\n",
@@ -275,8 +275,8 @@
"print(classification_report(y_test, lr_preds))\n", "print(classification_report(y_test, lr_preds))\n",
"\n", "\n",
"plt.figure(figsize=(10,7))\n", "plt.figure(figsize=(10,7))\n",
"# This methods outputs a visualization\n", "# This method outputs a visualization\n",
"# the h parameter adjusts the precision of the visualization\n", "# The h parameter adjusts the precision of the visualization\n",
"# if you find memory errors, set h to a higher value (e.g., h=0.1)\n", "# if you find memory errors, set h to a higher value (e.g., h=0.1)\n",
"plot_decision_surface(X, y, lr, h=0.02) " "plot_decision_surface(X, y, lr, h=0.02) "
] ]
@@ -535,11 +535,11 @@
"collapsed": true "collapsed": true
}, },
"source": [ "source": [
"We see that some classifiers (kNN, SVM) successfully learn the spiral problem. They can classify correctly in any part of the plane.\n", "We see that some classifiers (kNN, SVM) successfully learn the spiral problem. They can classify correctly at any point in the plane.\n",
"\n", "\n",
"Nevertheless, some classifiers (Logistic Regression, Gaussian Naive Bayes) are not able to learn the spiral pattern with their default configurations.\n", "Nevertheless, some classifiers (Logistic Regression, Gaussian Naive Bayes) are not able to learn the spiral pattern with their default configurations.\n",
"\n", "\n",
"In particular, the MLP performs very bad: it is not able to learn the spiral function. Nevertheless, it should be able to." "In particular, the MLP performs very badly: it is not able to learn the spiral function. Nevertheless, it should be able to."
] ]
}, },
{ {
@@ -578,7 +578,7 @@
"- regularization of the network\n", "- regularization of the network\n",
"- new features that are passed to the network\n", "- new features that are passed to the network\n",
"\n", "\n",
"You can search inspiration on [this playground](http://playground.tensorflow.org)." "You can search for inspiration on [this playground](http://playground.tensorflow.org)."
] ]
}, },
{ {
@@ -621,7 +621,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n", "\n",
"© Óscar Araque, Universidad Politécnica de Madrid." "© Óscar Araque, Universidad Politécnica de Madrid."
] ]