# How can we calculate inaccuracy if our data is biased?

To calculate the inaccuracy of a model in `Scikit-Learn` when the data is biased, you can use the following steps:

1. Split the data into training and testing sets using the `train_test_split` function.
2. Fit your model to the training data using `fit` method.
3. Make predictions on the testing data using `predict` method.
4. Calculate the confusion matrix using `confusion_matrix` function.
5. Calculate the accuracy, precision, recall, and F1 score using `accuracy_score`, `precision_score`, `recall_score`, and `f1_score` functions respectively.

Here’s an example code to calculate the inaccuracy of a model when the data is biased in Scikit-learn:

In this example, we load the iris dataset using the `load_iris` function and split it into training and testing sets using the `train_test_split` function. We then fit a logistic regression model to the training data, make predictions on the testing data, and calculate the confusion matrix, accuracy, precision, recall, and F1 score.
Note that we use the `average='macro'` argument in the precision, recall, and F1 score functions to compute the metrics for each class and take their unweighted mean.

#### 1. Using (ROC) curve and the area under the ROC curve (AUC):

The `ROC` curve is a graphical representation of the performance of a binary classifier as the discrimination threshold is varied. The `AUC` is a metric that measures the performance of the model regardless of the chosen threshold.

Here’s an example code to calculate the ROC curve and AUC of a model when the data is biased in Scikit-learn:

The ROC curve is plotted using `fpr` (false positive rate) as the x-axis and `tpr` (true positive rate) as the y-axis. The AUC is a value between 0 and 1, where a value of 0.5 indicates a random classifier and a value of 1.0 indicates a perfect classifier. The higher the AUC, the better the model is at distinguishing between positive and negative samples.