Updated the precision definition.

02_pytorch_classification.ipynb

@@ -3635,7 +3635,7 @@
     "| **Metric name/Evaluation method** | **Defintion** | **Code** |\n",
     "| --- | --- | --- |\n",
     "| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | [`torchmetrics.Accuracy()`](https://torchmetrics.readthedocs.io/en/stable/classification/accuracy.html#id3) or [`sklearn.metrics.accuracy_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) |\n",
-    "| Precision | Proportion of true positives over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`torchmetrics.Precision()`](https://torchmetrics.readthedocs.io/en/stable/classification/precision.html#id4) or [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) |\n",
+    "| Precision | Proportion of true positives over total number of true positives and false positives. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`torchmetrics.Precision()`](https://torchmetrics.readthedocs.io/en/stable/classification/precision.html#id4) or [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) |\n",
     "| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should've been 1). Higher recall leads to less false negatives. | [`torchmetrics.Recall()`](https://torchmetrics.readthedocs.io/en/stable/classification/recall.html#id5) or [`sklearn.metrics.recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) |\n",
     "| F1-score | Combines precision and recall into one metric. 1 is best, 0 is worst. | [`torchmetrics.F1Score()`](https://torchmetrics.readthedocs.io/en/stable/classification/f1_score.html#f1score) or [`sklearn.metrics.f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) |\n",
     "| [Confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)  | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line). | [`torchmetrics.ConfusionMatrix`](https://torchmetrics.readthedocs.io/en/stable/classification/confusion_matrix.html#confusionmatrix) or [`sklearn.metrics.plot_confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay.from_predictions) |\n",