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docs(machine-learning-pipeline): fix audits format

DEV-4049-remove-alcohol-terminology
eslopfer 2 years ago
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  1. 44
      subjects/ai/pipeline/audit/README.md

44
subjects/ai/pipeline/audit/README.md
unescape_control_characters View File

@ -1,6 +1,6 @@
#### 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`.
@ -8,7 +8,7 @@
###### Does it print `Python 3.x`? x >= 8
##### Does `import jupyter`, `import numpy`, `import pandas`, `import matplotlib` and `import sklearn` run without any error?
###### Do `import jupyter`, `import numpy`, `import pandas`, `import matplotlib` and `import sklearn` run without any error?
---
@ -16,15 +16,15 @@
#### Exercise 1: Imputer 1
##### 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 the `imp_mean.statistics_` returns:
###### For question 1 is validated if the `imp_mean.statistics_` returns:
```console
array([ 4., 13., 6.])
```
##### The question 2 is validated if the filled train set is:
###### For question 2, is the filled train set the following?
```console
array([[ 7., 6., 5.],
@ -32,7 +32,7 @@
[ 1., 20., 8.]])
```
##### The question 3 is validated if the filled test set is:
###### For question 3, is the filled test set the following?
```console
array([[ 4., 1., 2.],
@ -46,9 +46,9 @@
#### Exercise 2: Scaler
##### 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 the scaled train set is as below. And by definition, the mean on the axis 0 should be `array([0., 0., 0.])` and the standard deviation on the axis 0 should be `array([1., 1., 1.])`.
###### For question 1, is the scaled train set as below? And by definition, the mean on the axis 0 should be `array([0., 0., 0.])` and the standard deviation on the axis 0 should be `array([1., 1., 1.])`.
```console
array([[ 0. , -1.22474487, 1.33630621],
@ -56,7 +56,7 @@ array([[ 0. , -1.22474487, 1.33630621],
[-1.22474487, 1.22474487, -1.06904497]])
```
##### The question 2 is validated if the scaled test set is:
###### For question 2, is the scaled test set the following?
```console
array([[ 1.22474487, -1.22474487, 0.53452248],
@ -70,9 +70,9 @@ array([[ 1.22474487, -1.22474487, 0.53452248],
#### Exercise 3: One hot Encoder
##### 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 the output is:
###### For question 1, is the output the following?
| | ('C++',) | ('Java',) | ('Python',) |
|---:|-----------:|------------:|--------------:|
@ -81,7 +81,7 @@ array([[ 1.22474487, -1.22474487, 0.53452248],
| 2 | 0 | 1 | 0 |
| 3 | 1 | 0 | 0 |
##### The question 2 is validated if the output is:
###### For question 2, is the output the following?
| | ('C++',) | ('Java',) | ('Python',) |
|---:|-----------:|------------:|--------------:|
@ -96,9 +96,9 @@ array([[ 1.22474487, -1.22474487, 0.53452248],
#### Exercise 4: Ordinal Encoder
##### 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 the output of the Ordinal Encoder on the train set is:
###### For question 1, is the output of the Ordinal Encoder on the train set the following?
```console
array([[2.],
@ -108,7 +108,7 @@ array([[2.],
Check that `enc.categories_` returns`[array(['bad', 'neutral', 'good'], dtype=object)]`.
##### The question 2 is validated if the output of the Ordinal Encoder on the test set is:
###### For question 2, is the output of the Ordinal Encoder on the test set the following?
```console
array([[2.],
@ -122,9 +122,9 @@ array([[2.],
#### Exercise 5: Categorical variables
##### 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 the number of unique values per feature outputted are:
###### For question 1, are the number of unique values per feature outputted the following?
```console
age 6
@ -139,7 +139,7 @@ irradiat 2
dtype: int64
```
##### The question 2 is validated if the transformed test set by the `OneHotEncoder` fitted on the train set is as below. Make sure the transformer takes as input a dataframe with the columns in the order defined `['node-caps' , 'breast', 'breast-quad', 'irradiat']` :
###### For question 2, is the transformed test set by the `OneHotEncoder` fitted on the train set as below? Make sure the transformer takes as input a dataframe with the columns in the order defined `['node-caps' , 'breast', 'breast-quad', 'irradiat']` :
```console
#First 10 rows:
@ -157,7 +157,7 @@ array([[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.],
```
##### The question 3 is validated if the transformed test set by the `OrdinalEncoder` fitted on the train set is as below with the columns ordered as `["menopause", "age", "tumor-size","inv-nodes", "deg-malig"]`:
###### For question 3, is the transformed test set by the `OrdinalEncoder` fitted on the train set as below with the columns ordered as `["menopause", "age", "tumor-size","inv-nodes", "deg-malig"]`?
```console
#First 10 rows:
@ -175,7 +175,7 @@ array([[1., 2., 5., 0., 1.],
```
##### The question 4 is validated if the column transformer transformed that is fitted on the X_train, transformed the X_test as:
###### For question 4, is the column transformer transformed that is fitted on the X_train, transformed the X_test as below?
```console
# First 2 rows:
@ -189,7 +189,7 @@ array([[1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 2., 5., 0., 1.],
#### Exercise 6: Pipeline
##### The question 1 is validated if the prediction on the test set are:
###### For question 1, are the predictions on the test set the following?
```console
array([0, 0, 2, 1, 2, 0, 2, 1, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 2, 2, 0, 0,
@ -197,6 +197,6 @@ array([0, 0, 2, 1, 2, 0, 2, 1, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 2, 2, 0, 0,
0, 1, 1, 1, 1, 1])
```
and the score on the test set is **98%**.
and is the score on the test set **98%**?
**Note: Keep in mind that having a 98% accuracy is not common when working with real life data. Every time you have a score > 97% check that there's no leakage in the data. On financial data set, the ratio signal to noise is low. Trying to forecast stock prices is a difficult problem. Having an accuracy higher than 70% should be interpreted as a warning to check data leakage!**

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