High bias

High variance

Too many features

Insufficient training data

To evaluate the performance of a model on unseen data

To select the optimal hyperparameters for a model

To prevent overfitting or underfitting

All of the above

Checking for data inconsistencies

Visualizing the model's predictions

Using a debugger to step through the model's code

Printing intermediate results during training

To select the best model for a given task

To identify potential biases in the model

To estimate the model's generalization error

All of the above

Type I error

Type II error

Overfitting

Underfitting

To reduce overfitting

To improve the model's generalization performance

To select the optimal hyperparameters for a model

All of the above

Increasing the amount of training data

Reducing the number of features

Applying regularization techniques

Early stopping

To increase the size of the training dataset

To improve the model's generalization performance

To reduce overfitting

All of the above

Accuracy

Precision

Recall

F1 score

To select the optimal values for a model's hyperparameters

To improve the model's generalization performance

To reduce overfitting or underfitting

All of the above

Selection bias

Confirmation bias

Sampling bias

Overfitting

To transform raw data into a format suitable for machine learning models

To improve the model's generalization performance

To reduce overfitting or underfitting

All of the above

Increasing the number of features

Applying regularization techniques

Early stopping

Increasing the amount of training data

To improve the model's generalization performance

To reduce overfitting or underfitting

To increase the model's robustness to noise and outliers

All of the above

Bagging

Boosting

Stacking

Dropout