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
- Fit your model to the training data using
- Make predictions on the testing data using
- Calculate the confusion matrix using
- Calculate the accuracy, precision, recall, and F1 score using
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):
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.