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Update 2_5_0_Machine_Learning.ipynb

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Carlos A. Iglesias
2026-02-25 16:32:10 +01:00
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ml1/2_5_0_Machine_Learning.ipynb
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@@ -72,7 +72,7 @@
"Machine learning algorithms are programs that learn a model from a dataset to make predictions or learn structures to organize the data.\n",
"\n",
"In scikit-learn, machine learning algorithms take as input a *numpy* array (n_samples, n_features), where\n",
"* **n_samples**: number of samples. Each sample is an item to process (i.e., classify). A sample can be a document, a picture, a sound, a video, a row in a database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
"* **n_samples**: number of samples. Each sample is an item to be processed (i.e., classified). A sample can be a document, a picture, a sound, a video, a row in a database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
"* **n_features**: The number of features or distinct traits that can be used to describe each item quantitatively.\n",
"\n",
"The number of features should be defined in advance. A specific type of feature set is high-dimensional (e.g., millions of features), but most values are zero for a given sample. Using (numpy) arrays, all those zero values would also take up memory. For this reason, these feature sets are often represented with sparse matrices (scipy.sparse) instead of (numpy) arrays.\n",
@@ -112,7 +112,7 @@
"metadata": {},
"source": [
"In *unsupervised machine learning models*, the machine learning model algorithm takes as input the feature vectors. It produces a predictive model that is used to fit its parameters to summarize the best regularities found in the data.\n",
"![](files/images/plot_ML_flow_chart_3.png)"
"![](./images/plot_ML_flow_chart_3.png)"
]
},
{
@@ -140,7 +140,7 @@
" * **model.fit_transform()**: Some estimators implement this method, which performs a fit and a transform on the same input data.\n",
"\n",
"\n",
"![](files/images/plot_ML_flow_chart_2.png)"
"![](./images/plot_ML_flow_chart_2.png)"
]
},
{