To calculate the inaccuracy of a model in `Scikit-Learn`

when the data is biased, you can use the following steps:

- Split the data into training and testing sets using the
`train_test_split`

function. - Fit your model to the training data using
`fit`

method. - Make predictions on the testing data using
`predict`

method. - Calculate the confusion matrix using
`confusion_matrix`

function. - 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.