Week2_S1

ROC curves plot which relationship?

• Precision vs. Recall

• Recall vs. Specificity

• True Positive Rate vs. False Positive Rate

• Precision vs. Accuracy

SVMs aim to maximize the (a)   between classes.

Random forest prediction for classification is based on:

• Single best tree

• Weighted averaging of features

• Averaging probabilities across trees

•  Taking the majority vote

A decision tree split that produces pure child nodes has:

• High entropy

•  Low entropy

• No information gain

• Random splits

Pruning a Decision Tree helps to:

•  Increase depth

• Reduce overfitting

• Increase overfitting

• Increase entropy

Which of the following explains why accuracy can be misleading for imbalanced datasets

• Accuracy equally weighs all errors.
  

• Accuracy is always better than precision/recall.

• Classifiers can achieve high accuracy by predicting the majority class only.

• Accuracy automatically adjusts for skewed classes

In logistic regression, the cost function commonly used is

• Sigmoid

•  Cross-entropy loss

• Mean squared error

• Hinge loss

The Perceptron differs from Logistic regression by using the (a)   activation instead of the sigmoid.

Which of the following statements correctly describes the relationship between biological and artificial neural networks?

• Artificial neural networks directly replicate the exact biological processes of the human brain.

• Biological neurons can be trained using gradient descent like artificial neurons.

• Artificial neurons are connected through synapses, just like biological neurons.

• Artificial neural networks simulate the learning mechanism of biological neurons using computational units and weights.

why has deep learning recently surpassed traditional machine learning methods in many tasks?

• Because deep learning completely eliminates the need for feature engineering.

• Because deep learning algorithms can solve more complex functions.

• Due to advancements in data availability, computational power, and experimentation.

• Because neural networks require less training data than traditional methods.