Semi-Supervised Learning QUIZ (MCQ QUESTIONS AND ANSWERS)

They can capture the underlying structure of the data

They can handle missing data more effectively

They can reduce the risk of overfitting

They can handle high-dimensional data more effectively

To ensure that the model's predictions are consistent across different training runs

To minimize the model's sensitivity to data augmentation

To better represent the underlying distribution of the data and reduce the risk of overfitting

To improve the model's convergence

Bootstrapping

Active learning

Consistency regularization

Data augmentation

High computational complexity

Limited applicability to certain problems

Risk of overfitting

Difficulty in handling high-dimensional data

The decision boundary should pass through low-density regions of the input space

Instances in the same cluster should have the same label

Similar input instances should have similar output labels

The input data lies on a low-dimensional manifold embedded in the high-dimensional input space

Better understanding of the data distribution

Better performance with limited labeled data

Better handling of missing data

Better generalization to new data

Data augmentation

Consistency regularization

Transfer learning

Active learning

Generative models capture the joint probability distribution of input and output variables, while discriminative models capture the conditional probability distribution of output variables given input variables

Generative models can handle high-dimensional data more effectively, while discriminative models struggle with high-dimensional data

Generative models require more labeled data, while discriminative models can work with less labeled data

Generative models can only be used for unsupervised learning, while discriminative models can be used for both supervised and semi-supervised learning

Better generalization to new data

More accurate predictions for the given unlabeled data

Faster training time

More interpretable models

When labeled data is abundant

When unlabeled data is scarce

When labeled data is scarce and unlabeled data is abundant

When both labeled and unlabeled data are scarce

Generative models

Graph-based methods

Deep learning models

Decision trees

The input data lies on a low-dimensional manifold embedded in the high-dimensional input space

The decision boundary should pass through low-density regions of the input space

Instances in the same cluster should have the same label

Similar input instances should have similar output labels

Smoothness assumption

Cluster assumption

Manifold assumption

Independence assumption

The input features are independent

The output labels are independent

The labeled and unlabeled data share the same underlying distribution

The model's parameters are normally distributed

The risk of propagating incorrect labels

The need for large amounts of labeled data

The limited applicability to certain problems

The risk of underfitting

To improve classification performance by using both labeled and unlabeled data

To reduce the amount of labeled data needed for training

To identify clusters within the data

To optimize model parameters using a reward signal

To minimize the model's sensitivity to data augmentation

To ensure the model's predictions are consistent across different training runs

To regularize the model's weights

To improve the model's convergence

Ensuring the model's predictions are consistent across different training runs

Minimizing the model's sensitivity to data augmentation

Avoiding the propagation of incorrect labels

Handling high-dimensional data effectively

Data augmentation

Bootstrapping

Transfer learning

Active learning

Improved performance with limited labeled data

Faster training time

Reduced overfitting

Better generalization

It assumes that instances in the same cluster should have the same label

It assumes that instances in different clusters should have different labels

It assumes that the number of clusters should be equal to the number of labels

It assumes that the decision boundary should pass through low-density regions of the input space

Self-training

Co-training

Label propagation

Random forest

Labeling instances by clustering them based on their features

Labeling instances based on the predictions of a model trained on labeled data

Labeling instances based on their proximity to other instances in the input space

Labeling instances based on their similarity to a prototype instance

They provide a better understanding of the data distribution

They can handle missing data more effectively

They can improve the performance of the model with less labeled data

They can handle high-dimensional data more effectively

Training a single model on both labeled and unlabeled data

Training two models on different views of the data, and using their predictions to label unlabeled instances

Training a model on labeled data, and then refining it using unlabeled data

Training a model on labeled data, and then using it to predict labels for unlabeled instances

The risk of propagating incorrect labels

The difficulty of combining labeled and unlabeled data

The risk of overfitting

The need for large amounts of labeled data

Transductive learning predicts labels only for the given unlabeled data, while inductive learning predicts labels for any new data

Transductive learning is based on labeled data only, while inductive learning uses both labeled and unlabeled data

Transductive learning is a type of unsupervised learning, while inductive learning is a type of supervised learning

Transductive learning is a type of reinforcement learning, while inductive learning is a type of supervised learning

By training a model on labeled data and using it to predict labels for unlabeled data, which are then added to the training set

By training multiple models on different subsets of the labeled data and averaging their predictions

By training a model on labeled data and iteratively refining its parameters using an optimization algorithm

By training a model on labeled data and using a validation set to tune its hyperparameters

Label propagation

Clustering

Bagging

Boosting

Labeled and unlabeled data should be uniformly distributed

The decision boundary should pass through low-density regions of the input space

The model should output similar labels for similar inputs

The model should have a smooth loss function