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Update 2_5_1_kNN_Model.ipynb

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Carlos A. Iglesias 2025-06-02 17:11:45 +03:00 committed by GitHub
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ml1/2_5_1_kNN_Model.ipynb
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@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![](files/images/EscUpmPolit_p.gif \"UPM\")"
"![](./images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
@ -55,7 +55,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The goal of this notebook is to learn how to train a model, make predictions with that model and evaluate these predictions.\n",
"The goal of this notebook is to learn how to train a model, make predictions with that model, and evaluate these predictions.\n",
"\n",
"The notebook uses the [kNN (k nearest neighbors) algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)."
]
@ -212,14 +212,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Precision, recall and f-score"
"### Precision, recall, and f-score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n",
"For evaluating classification algorithms, we usually calculate three metrics: precision, recall, and F1-score\n",
"\n",
"* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n",
"* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n",
@ -246,7 +246,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Another useful metric is the confusion matrix"
"Another useful metric is the confusion matrix."
]
},
{
@ -262,7 +262,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We see we classify well all the 'setosa' and 'versicolor' samples. "
"We classify all the 'setosa' and 'versicolor' samples well. "
]
},
{
@ -276,7 +276,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**."
"To avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**."
]
},
{
@ -298,7 +298,7 @@
"# create a k-fold cross validation iterator of k=10 folds\n",
"cv = KFold(10, shuffle=True, random_state=33)\n",
"\n",
"# by default the score used is the one returned by score method of the estimator (accuracy)\n",
"# by default the score used is the one returned by the score method of the estimator (accuracy)\n",
"scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
"print(scores)"
]
@ -307,7 +307,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure"
"We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure."
]
},
{
@ -340,7 +340,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to tune the algorithm, and calculate which is the best value for the k hyperparameter."
"We will tune the algorithm and calculate the best value for the k hyperparameter."
]
},
{
@ -365,7 +365,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The result is very dependent of the input data. Execute again the train_test_split and test again how the result changes with k."
"The result is very dependent on the input data. Execute the train_test_split again and test how the result changes with k."
]
},
{
@ -387,7 +387,7 @@
"metadata": {},
"source": [
"## Licence\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",
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
]