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docs(clasification-with-scikit-learn): fix audits format

DEV-4049-remove-alcohol-terminology
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  1. 54
      subjects/ai/classification/audit/README.md

54
subjects/ai/classification/audit/README.md
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@ -1,14 +1,14 @@
#### Exercise 0: Environment and libraries
##### The exercice is validated is all questions of the exercice are validated.
##### The exercise is validated is all questions of the exercise are validated.
##### Activate the virtual environment. If you used `conda` run `conda activate your_env`
##### Run `python --version`
###### Does it print `Python 3.x`? x >= 8
###### Does it print `Python 3.x`? x >= 8?
###### Does `import jupyter`, `import numpy`, `import pandas`, `import matplotlib` and `import sklearn` run without any error ?
###### Does `import jupyter`, `import numpy`, `import pandas`, `import matplotlib` and `import sklearn` run without any error?
---
@ -16,11 +16,11 @@
#### Exercise 1: Logistic regression with Scikit-learn
##### The question 1 is validated if the predicted class is `0`.
###### Is the predicted class for question 1 `0`?
##### The question 2 is validated if the predicted probabilities are `[0.61450526 0.38549474]`
###### Are the predicted probabilities for question 2 `[0.61450526 0.38549474]`?
##### The question 3 is validated if the output is:
###### Is the output for question 3 like this?
```console
Coefficient:
@ -31,13 +31,14 @@ Score:
0.7142857142857143
```
---
---
#### Exercise 2: Sigmoid
##### The question 1 is validated if the plot looks like this:
###### Does the plot for question 1 look like this?
![alt text][ex2q1]
@ -49,38 +50,38 @@ Score:
#### Exercise 3: Decision boundary
##### The exercice is validated is all questions of the exercice are validated
##### The exercise is validated is all questions of the exercise are validated
##### The question 1 is validated if the outputted plot looks like this:
###### Does the outputted plot for question 1 look like this?
![alt text][ex3q1]
[ex3q1]: ../w2_day2_ex3_q1.png "Scatter plot"
##### The question 2 is validated if the coefficient and the intercept of the Logistic Regression are:
###### Are the coefficient and the intercept of the Logistic Regression for question 2 these?
```console
Intercept: [-0.98385574]
Coefficient: [[1.18866075]]
```
##### The question 3 is validated if the plot looks like this:
###### Does the plot for question 3 look like this?
![alt text][ex3q2]
[ex3q2]: ../w2_day2_ex3_q3.png "Scatter plot"
##### The question 4 is validated if `predict_probability` outputs the same probabilities as `predict_proba`. Note that the values have to match one of the class probabilities, not both. To do so, compare the output with: `clf.predict_proba(X)[:,1]`. The shape of the arrays is not important.
###### For question 4, does `predict_probability` output the same probabilities as `predict_proba`? Note that the values have to match one of the class probabilities, not both. To do so, compare the output with: `clf.predict_proba(X)[:,1]`. The shape of the arrays is not important.
##### The question 5 is validated if `predict_class` outputs the same classes as `cfl.predict(X)`. The shape of the arrays is not important.
###### Does `predict_class` output the same classes as `cfl.predict(X)` for question 5? The shape of the arrays is not important.
##### The question 6 is validated if the plot looks like the plot below. As mentioned, it is not required to shift the class prediction to make the plot easier to understand.
###### Does the plot for question 6 look like the plot below? As mentioned, it is not required to shift the class prediction to make the plot easier to understand.
![alt text][ex3q6]
[ex3q6]: ../w2_day2_ex3_q5.png "Scatter plot + Logistic regression + predictions"
##### The question 7 is validated if the plot looks like this:
###### Does the plot look like this for question 7?
![alt text][ex3q7]
@ -92,9 +93,9 @@ Coefficient: [[1.18866075]]
#### Exercise 4: Train test split
##### The exercise is validated is all questions of the exercise are validated
##### The exercise is validated if all questions of the exercise are validated
##### The question 1 is validated if X_train, y_train, X_test, y_test match the output below. The proportion of class `1` is **0.125** in the train set and **1.** in the test set.
###### Do X_train, y_train, X_test, y_test match the output below for question 1? The proportion of class `1` is **0.125** in the train set and **1.** in the test set.
```console
X_train:
@ -121,7 +122,7 @@ y_test:
[1. 1.]
```
##### The question 2 is validated if the proportion of class `1` is **0.3** for both sets.
###### Is the proportion of class `1` **0.3** for both sets in question 2?
---
@ -129,16 +130,16 @@ y_test:
#### Exercise 5: Breast Cancer prediction
##### The exercice is validated is all questions of the exercice are validated
###### The exercise is validated is all questions of the exercise are validated
##### The question 1 is validated if the proportion of class `Benign` is 0.6552217453505007. It means that if you always predict `Benign` your accuracy would be 66%.
###### Is the proportion of class `Benign` 0.6552217453505007 for question 1? It means that if you always predict `Benign` your accuracy would be 66%.
##### The question 2 is validated if the proportion of one of the classes is the approximately the same on the train and test set: ~0.65. In my case:
###### Is the proportion of one of the classes approximately the same on the train and test set: ~0.65 for question 2? In my case:
- test: 0.6571428571428571
- train: 0.6547406082289803
##### The question 3 is validated if the output is:
###### Is this the output for question 3?
```console
# Train
@ -171,7 +172,7 @@ For some reasons, you may have a different data splitting as mine. The requireme
If the score is 1, congratulate you peer, he's just leaked his first target. The target should be dropped from the X_train or X_test ;) !
##### The question 4 is validated if the confusion matrix on the train set is similar to:
###### Is the confusion matrix on the train set similar to this in question 4?
```console
array([[357, 9],
@ -191,11 +192,12 @@ As said, for some reasons, the results may be slightly different from mine becau
---
#### Bonus
#### Exercise 6: Multi-class (Optional)
##### The exercice is validated is all questions of the exercice are validated
##### The exercise is validated is all questions of the exercise are validated
##### The question 1 is validated if each classifier has as input a binary data as below:
###### Does each classifier have as input a binary data as below for question 1?
```python
def train(X_train, y_train):
@ -210,7 +212,7 @@ def train(X_train, y_train):
return clf, clf1, clf2
```
##### The question 2 is validated if the predicted classes on the test set are:
###### Are this the predicted classes on the test set for question 2?
```console
array([0, 0, 2, 1, 2, 0, 2, 1, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 2, 2, 0, 0,

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