Feature Engineering and Model Selection

What is feature selection in machine learning?

• Feature selection is the method of eliminating all features from the model.

• Feature selection in machine learning is the process of selecting a subset of relevant features for model training.

• Feature selection involves randomly selecting features without any criteria.

• Feature selection is the process of increasing the number of features for better accuracy.

What is the purpose of hyperparameter tuning?

• To eliminate the need for feature selection.

• The purpose of hyperparameter tuning is to optimize model performance.

• To increase the complexity of the model.

• To reduce the size of the dataset.

What is cross-validation and why is it used?

• Cross-validation is a method for increasing the size of a dataset.

• Cross-validation is used to improve the speed of model training.

• Cross-validation is a technique for visualizing data distributions.

• Cross-validation is used to evaluate the performance of a model and to prevent overfitting.

Describe k-fold cross-validation.

• K-fold cross-validation involves training on the entire dataset without any testing.

• K-fold cross-validation is a model validation technique that divides the dataset into 'k' subsets, training on 'k-1' folds and testing on the remaining fold, repeated 'k' times.

• K-fold cross-validation is a technique that uses a single subset for both training and testing.

• K-fold cross-validation is a method that only requires one fold for validation.

What is the role of the validation set in model selection?

• The validation set is only for testing the final model's accuracy.

• The validation set is not necessary if you have a large training set.

• The validation set is used to train the model on all available data.

• The validation set helps in tuning hyperparameters and selecting the best model by evaluating performance on unseen data.

How does feature importance help in feature selection?

• Feature importance is only useful for regression models.

• Feature importance eliminates the need for data preprocessing.

• Feature importance helps in selecting the most relevant features for model training.

• Feature importance guarantees better model accuracy.

What is the difference between overfitting and underfitting?

• Overfitting is excessive learning from training data, while underfitting is insufficient learning from data.

• Overfitting is when a model performs poorly on training data, while underfitting is when it performs well on training data.

• Overfitting and underfitting are both terms used to describe the same issue in machine learning.

• Overfitting occurs when a model is too simple, while underfitting is when it is too complex.

How can cross-validation help in preventing overfitting?

• Cross-validation eliminates the need for regularization techniques.

• Cross-validation increases the training data size, leading to overfitting.

• Cross-validation provides a more reliable estimate of model performance, reducing the risk of overfitting.

• Cross-validation only works with linear models, not complex ones.

What is a confusion matrix and how is it used in model evaluation?

• A confusion matrix only displays the accuracy of a model.

• A confusion matrix is a tool for optimizing hyperparameters in machine learning.

• A confusion matrix is used to visualize regression model performance.

• A confusion matrix is a tool for evaluating classification model performance by displaying true and false predictions.

What is the purpose of the test set in machine learning?

• To train the model on labeled data.

• The purpose of the test set in machine learning is to evaluate the model's performance on unseen data.

• To store the model's parameters.

• To optimize the model's hyperparameters.