Week2_S1

Precision vs. Recall

Recall vs. Specificity

True Positive Rate vs. False Positive Rate

Precision vs. Accuracy

Single best tree

Weighted averaging of features

Averaging probabilities across trees

 Taking the majority vote

High entropy

 Low entropy

No information gain

Random splits

 Increase depth

Reduce overfitting

Increase overfitting

Increase entropy

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

Sigmoid

 Cross-entropy loss

Mean squared error

Hinge loss

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.

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.