diff --git a/Regression/4-Logistic/README.md b/Regression/4-Logistic/README.md
index 15c07a0f9..8a0c3bda1 100644
--- a/Regression/4-Logistic/README.md
+++ b/Regression/4-Logistic/README.md
@@ -169,33 +169,46 @@ Predicted labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 1 0 1 0 0 1 0 0 0 1 0]
```
-Let's unpack some of those [terms](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) with a confusion matrix to help us measure the performance of our mdoel:
+## Better comprehension via a confusion matrix
+
+While you can get a scoreboard report [terms](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) by printing out the items above, you might be able to understand your model more easily by using a [confusion matrix]() to help us understand how the model is performing.
+
+> 🎓 A '[confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix)' (or 'error matrix') is a table that expresses your model's true vs. false positives and negatives, thus gauging the accuracy of predictions.
```python
from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, predictions)
```
-Take a look at our confusion matrix:
+Take a look at your model's confusion matrix:
```
array([[162, 4],
[ 33, 0]])
```
-Let's understand what these numbers mean with an example. Let's say out model can classify between two categories, category 0 and category 1. If your model predicts something as category 0 and it belongs to category 0 in reality we call it a true positive, shown by the top left number. If your model predicts something as category 1 and it belongs to category 0 in reality we call it a false positive, shown by the top right number. If your model predicts something as category 0 and it belongs to category 1 in reality we call it a false negative, shown by the bottom left number. If your model predicts something as category 0 and it belongs to category 0 in reality we call it a true negative, shown by the top left number.
+What's going on here? Let's say our model is asked to classify items between two binary categories, category 'pumpkin' and category 'not-a-pumpkin'.
+
+- If your model predicts something as a pumpkin and it belongs to category 'pumpkin' in reality we call it a true positive, shown by the top left number.
+- If your model predicts something as not a pumpkin and it belongs to category 'pumpkin' in reality we call it a false positive, shown by the top right number.
+- If your model predicts something as a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a false negative, shown by the bottom left number.
+- If your model predicts something as not a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a true negative, shown by the bottom right number.
-
+
-As you might have guessed we like to have a larger number of true positives and true negatives and a lower number of false negatives and false positives, which implies that the model performs better.
+> Infographic by [Jen Looper](https://twitter.com/jenlooper)
-Let's now understand more about the terms we saw earlier with the help of confusion matrix:
+As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
+
+✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true positives but also several false negatives.
+
+Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
🎓 Precision: TP/(TP + FN) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
🎓 Recall: TP/(TP + FP) The fraction of relevant instances that were retrieved, whether well-labeled or not
-🎓 f1-score: (2 * precison * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0
+🎓 f1-score: (2 * precision * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0
🎓 Support: The number of occurrences of each label retrieved
@@ -205,8 +218,7 @@ Let's now understand more about the terms we saw earlier with the help of confus
🎓 Weighted Avg: The calculation of the mean metrics for each label, taking label imbalance into account by weighting them by their support (the number of true instances for each label).
-> Can you think which metric you should use if you want your model to reduce the number of false negatives?
-
+✅ Can you think which metric you should watch if you want your model to reduce the number of false negatives?
## Visualize the ROC Curve of this Model
This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables.
diff --git a/Regression/4-Logistic/images/confusion-matrix.png b/Regression/4-Logistic/images/confusion-matrix.png
index e19b0331e..240256bc7 100644
Binary files a/Regression/4-Logistic/images/confusion-matrix.png and b/Regression/4-Logistic/images/confusion-matrix.png differ
diff --git a/Regression/4-Logistic/solution/notebook.ipynb b/Regression/4-Logistic/solution/notebook.ipynb
index 71bc5604f..be24dd5a3 100644
--- a/Regression/4-Logistic/solution/notebook.ipynb
+++ b/Regression/4-Logistic/solution/notebook.ipynb
@@ -15,175 +15,8 @@
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+ "output_type": "execute_result",
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@@ -207,11 +40,11 @@
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@@ -254,6 +87,7 @@
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]
},
- "execution_count": 3,
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+ "execution_count": 3
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],
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},
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\n",
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