AI-basic 2

Training models only on unlabeled data

Using labeled examples to learn a function or classifier

Guessing class labels without training

Ignoring training data

Records with attributes and known class labels

Only unlabeled raw data

No attributes, just random numbers

Class labels hidden from the learning algorithm

Creates a tree by splitting data based on attribute values

Requires no attributes

Cannot be pruned once grown

Ignores impurity measures entirely

Generalizes well to new, unseen data

Fits training data too closely, harming generalization

Is always the best model

Occurs only when trees have no leaves

The same quantity in classification performance

Different aspects of predictive accuracy (true positives and coverage of actual positives)

Only false negatives

Irrelevant metrics in model evaluation

Uses multiple train/test splits to better estimate model performance

Trains on the entire dataset without testing

Is never used in practice

Is identical to using a holdout set

Find a hyperplane that separates classes with the maximum margin

Never use kernels

Do not allow for soft margins

Are only applicable to linear problems

Builds a model by averaging the target function parameters ahead of time

Is a lazy learner that classifies based on proximity to known examples

Uses complex parametric training

Cannot handle numeric attributes

Combine multiple models to often improve predictive performance

Always perform worse than single models

Never use bootstrapped samples or sequential weighting

Cannot handle classification tasks

The ratio of correct predictions to total predictions

The harmonic mean of precision and recall

Ignored in model evaluation

Always equal to accuracy