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Update 2_4_Preprocessing.ipynb

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Carlos A. Iglesias 2025-06-02 16:29:26 +03:00 committed by GitHub
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ml1/2_4_Preprocessing.ipynb
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@ -4,7 +4,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"![](files/images/EscUpmPolit_p.gif \"UPM\")" "![](./images/EscUpmPolit_p.gif \"UPM\")"
] ]
}, },
{ {
@ -76,7 +76,7 @@
"source": [ "source": [
"A common practice in machine learning to evaluate an algorithm is to split the data at hand into two sets, one that we call the **training set** on which we learn data properties and one that we call the **testing set** on which we test these properties. \n", "A common practice in machine learning to evaluate an algorithm is to split the data at hand into two sets, one that we call the **training set** on which we learn data properties and one that we call the **testing set** on which we test these properties. \n",
"\n", "\n",
"We are going to use *scikit-learn* to split the data into random training and testing sets. We follow the ratio 75% for training and 25% for testing. We use `random_state` to ensure that the result is always the same and it is reproducible. (Otherwise, we would get different training and testing sets every time)." "We will use *scikit-learn* to split the data into random training and testing sets. We follow the ratio 75% for training and 25% for testing. We use `random_state` to ensure that the result is always the same and it is reproducible. (Otherwise, we would get different training and testing sets every time)."
] ]
}, },
{ {
@ -122,9 +122,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Standardization of datasets is a common requirement for many machine learning estimators implemented in the scikit; they might behave badly if the individual features do not more or less look like standard normally distributed data: Gaussian with zero mean and unit variance.\n", "Standardization of datasets is a common requirement for many machine learning estimators implemented in the scikit; they might misbehave if the individual features do not more or less look like standard normally distributed data: Gaussian with zero mean and unit variance.\n",
"\n", "\n",
"The preprocessing module further provides a utility class `StandardScaler` to compute the mean and standard deviation on a training set. Later, the same transformation will be applied on the testing set." "The preprocessing module further provides a utility class `StandardScaler` to compute a training set's mean and standard deviation. Later, the same transformation will be applied on the testing set."
] ]
}, },
{ {
@ -173,7 +173,7 @@
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Licences\n", "### Licences\n",
"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",
"© Carlos A. Iglesias, Universidad Politécnica de Madrid." "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
] ]