Accuracy

Precision

Recall

R-squared

Credit scoring

Fraud detection

Image recognition

Medical diagnosis

Accuracy

Precision

Recall

R-squared

Ridge regression

K-nearest neighbors

Decision trees

Support vector machines

Grid search

Random search

Bayesian optimization

Naive Bayes

Elastic net regularization

Ridge regularization

L2 regularization

L1 regularization

To represent the effect of the input features on the output

To improve the accuracy of the model

To account for the bias in the data

To add complexity to the model

Oversampling the minority class

Under sampling the majority class

Using a different performance metric, such as AUC

Ignoring the class imbalance

To measure the accuracy of the model

To penalize large coefficient values

To minimize the difference between predicted and actual values

To regularize the model to prevent overfitting

To visualize the decision boundary of the model

To evaluate the overall accuracy of the model

To identify the outliers in the data

To evaluate the tradeoff between sensitivity and specificity