diff --git a/subjects/ai/training/audit/README.md b/subjects/ai/training/audit/README.md index b9895d789..13665d199 100644 --- a/subjects/ai/training/audit/README.md +++ b/subjects/ai/training/audit/README.md @@ -16,14 +16,9 @@ #### Exercise 1: MSE Scikit-learn -##### The goal of this exercise is to learn to use `sklearn.metrics` to compute the mean squared error (MSE). +###### Is the Mean Squared Error (MSE) calculated using `sklearn.metrics` library? -1. Compute the MSE using `sklearn.metrics` on `y_true` and `y_pred` below: - -```python -y_true = [91, 51, 2.5, 2, -5] -y_pred = [90, 48, 2, 2, -4] -``` +###### Is the Mean Squared Error (MSE) correctly computed for the given `y_true` and `y_pred` values, and does the calculated MSE match the expected value? --- @@ -31,14 +26,9 @@ y_pred = [90, 48, 2, 2, -4] #### Exercise 2: Accuracy Scikit-learn -##### The goal of this exercise is to learn to use `sklearn.metrics` to compute the accuracy. +###### Is the accuracy computed using `sklearn.metrics` library? -1. Compute the accuracy using `sklearn.metrics` on `y_true` and `y_pred` below: - -```python -y_pred = [0, 1, 0, 1, 0, 1, 0] -y_true = [0, 0, 1, 1, 1, 1, 0] -``` +###### Is the accuracy correctly calculated for the given `y_true` and `y_pred` values, and does the calculated accuracy match the expected value? --- @@ -125,7 +115,7 @@ array([[37, 2], ![alt text][logo_ex4] -[logo_ex4]: ../w2_day4_ex4_q3.png "ROC AUC " +[logo_ex4]: ../w2_day4_ex4_q3.png 'ROC AUC ' Having a 99% ROC AUC is not usual. The data set we used is easy to classify. On real data sets, always check if there's any leakage while having such a high ROC AUC score.