#### Exercise 0: Environment and libraries
##### The exercise is validated if 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 `import jupyter`, `import numpy`, `import pandas`, `import matplotlib` and `import sklearn` run without any error?
---
---
#### Exercise 1: Logistic regression with Scikit-learn
###### Is the predicted class for question 1 `0`?
###### Are the predicted probabilities for question 2 `[0.61450526 0.38549474]`?
###### Is the output for question 3 like this?
```console
Coefficient:
[[0.81786797]]
Intercept:
[-0.87522391]
Score:
0.7142857142857143
```
---
---
#### Exercise 2: Sigmoid
###### Does the plot for question 1 look like this?
![alt text][ex2q1]
[ex2q1]: ../w2_day2_ex2_q1.png "Scatter plot"
---
---
#### Exercise 3: Decision boundary
##### The exercise is validated if all questions of the exercise are validated
###### Does the outputted plot for question 1 look like this?
![alt text][ex3q1]
[ex3q1]: ../w2_day2_ex3_q1.png "Scatter plot"
###### Are the coefficient and the intercept of the Logistic Regression for question 2 these?
```console
Intercept: [-0.98385574]
Coefficient: [[1.18866075]]
```
###### Does the plot for question 3 look like this?
![alt text][ex3q2]
[ex3q2]: ../w2_day2_ex3_q3.png "Scatter plot"
###### 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.
###### Does `predict_class` output the same classes as `cfl.predict(X)` for question 5? The shape of the arrays is not important.
###### 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"
###### Does the plot look like this for question 7?
![alt text][ex3q7]
[ex3q7]: ../w2_day2_ex3_q6.png "Logistic regression decision boundary"
---
---
#### Exercise 4: Train test split
##### The exercise is validated if all questions of the exercise are validated
###### 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:
[[ 1 2]
[ 3 4]
[ 5 6]
[ 7 8]
[ 9 10]
[11 12]
[13 14]
[15 16]]
y_train:
[0. 0. 0. 0. 0. 0. 0. 1.]
X_test:
[[17 18]
[19 20]]
y_test:
[1. 1.]
```
###### Is the proportion of class `1` **0.3** for both sets in question 2?
---
---
#### Exercise 5: Breast Cancer prediction
###### The exercise is validated if all questions of the exercise are validated
###### Is the proportion of class `Benign` 0.6552217453505007 for question 1? It means that if you always predict `Benign` your accuracy would be 66%.
###### 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
###### Is this the output for question 3?
```console
# Train
Class prediction on train set:
[4 2 4 2 2 2 2 4 2 2]
Probability prediction on train set:
[0.99600415 0.00908666 0.99992744 0.00528803 0.02097154 0.00582772
0.03565076 0.99515326 0.00788281 0.01065484]
Score on train set:
0.9695885509838998
#Test
Class prediction on test set:
[2 2 2 4 2 4 2 2 2 4]
Probability prediction on test set:
[0.01747203 0.22495309 0.00698756 0.54020801 0.0015289 0.99862249
0.33607994 0.01227679 0.00438157 0.99972344]
Score on test set:
0.9642857142857143
```
Only the 10 first predictions are outputted. The score is computed on all the data in the folds.
For some reasons, you may have a different data splitting as mine. The requirement for this question is to have a score on the test set bigger than 92%.
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 ;) !
###### Is the confusion matrix on the train set similar to this in question 4?
```console
array([[357, 9],
[ 8, 185]])
```
and if the confusion matrix on the test set is similar to:
```console
array([[90, 2],
[ 3, 45]])
```
As said, for some reasons, the results may be slightly different from mine because of the data splitting. However, the values in the confusion matrix should be close to these results.
---
---
#### Bonus
#### Exercise 6: Multi-class (Optional)
##### The exercise is validated if all questions of the exercise are validated
###### +Does each classifier have as input a binary data as below for question 1?
```python
def train(X_train, y_train):
clf = LogisticRegression()
clf1 = LogisticRegression()
clf2 = LogisticRegression()
clf.fit(X_train, y_train == 0)
clf1.fit(X_train, y_train == 1)
clf2.fit(X_train, y_train == 2)
return clf, clf1, clf2
```
###### +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,
0, 2, 2, 2, 0, 1, 0, 0])
```
Even if I had this warning `ConvergenceWarning: lbfgs failed to converge (status=1):` I noticed that `LogisticRegression` returns the same output.
