Machine Learning Model Evaluation and Improvement

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What is the recommended percentage split for training and testing data?

80% for training and 20% for testing

70% for training and 30% for testing (correct)

50% for training and 50% for testing

60% for training and 40% for testing

Why is it important to use new data when evaluating a model?

To train the model on a larger dataset

To increase the model's complexity

To prevent overfitting to the training set (correct)

To speed up the evaluation process

What is the purpose of a validation set in machine learning?

To train the model on additional data

To evaluate the model while building and tuning it (correct)

To measure the model's performance on the training data

To use as the final test set

Why should the model not be trained on the entire dataset?

To prevent overfitting to the training set (D) Signup and view all the answers

What risk is associated with using the test set to select model parameters?

The risk of overfitting (A) Signup and view all the answers

What happens if the model is tuned based on performance only on the test data?

The model may overfit to the test set (D) Signup and view all the answers

Why is squared error commonly used in machine learning?

Because it reports that the prediction was incorrect regardless of whether it was too high or too low (C) Signup and view all the answers

What does the R2 coefficient represent in machine learning?

The proportion of variance in the outcome that the model can predict based on its features (B) Signup and view all the answers

What happens when a machine learning model has high bias?

It underfits the data and is limited from learning the true trend (C) Signup and view all the answers

What is the purpose of validation curves in machine learning?

To diagnose whether a model is suffering from high bias or high variance (D) Signup and view all the answers

What does a gap between the training and validation error in learning curves indicate?

High variance (A) Signup and view all the answers

What is a common consequence of models with high variance?

Overfitting of the data (D) Signup and view all the answers

How can models suffering from high bias be improved?

By adding additional features to the dataset (C) Signup and view all the answers

What is one common use of reducing a dataset into two dimensions when evaluating a classifier model?

To visualize observations and decision boundary for performance evaluation (D) Signup and view all the answers

Which region in a validation curve indicates that a model is subject to high bias?

When both training and validation errors are high (B) Signup and view all the answers

What does underfitting refer to in machine learning?

Limitation from learning the true trend and performing poorly on new data (C) Signup and view all the answers

What is an appropriate approach for improving models that suffer from high variance?

Feeding more data during training (A) Signup and view all the answers

When will training on more data do very little to improve a model with high bias?

When models underfit the data and pay little attention to it (D) Signup and view all the answers

What percentage of the data is typically used for training in a train/test/validation split?

60% (A) Signup and view all the answers

Which metric is defined as the percentage of correct predictions for the test data?

Accuracy (B) Signup and view all the answers

What fraction is precision defined as?

True positives / (True positives + False positives) (D) Signup and view all the answers

In which scenario is recall important?

When developing a classification algorithm for disease prediction (A) Signup and view all the answers

What is the common approach for combining precision and recall metrics?

F-score (B) Signup and view all the answers

Why do we have a different set of evaluation metrics for regression models compared to classification models?

Regression models predict in a continuous range while classification models predict in discrete classes (B) Signup and view all the answers

What does explained variance metric represent?

The amount of variation in the original dataset that our model is able to explain (D) Signup and view all the answers

What does mean squared error measure?

The average of squared differences between the predicted output and the true output (A) Signup and view all the answers

Which metric compares the variance within the expected outcomes to the variance in the error of a regression model?

Explained variance (D) Signup and view all the answers

Which parameter allows us to control the tradeoff of importance between precision and recall?

Beta (C) Signup and view all the answers

What should be done before making splits in a train/test/validation scenario to ensure an accurate representation of the dataset?

Shuffle the data (B) Signup and view all the answers

Why are precision and recall useful in cases where classes aren't evenly distributed?

To ensure balanced predictions despite class imbalance (B) Signup and view all the answers