Supervised Learning QUIZ (MCQ QUESTIONS AND ANSWERS)

To search for the best combination of hyperparameters for a given model

To split the data into training and testing sets

To select the best features for a model

To combine multiple models into an ensemble

Mean squared error

Precision, recall, and F1-score

R-squared

Mean absolute error

Mean squared error

Precision, recall, and F1-score

R-squared

Mean absolute error

To control the model's complexity

To shift the decision boundary or regression line

To scale the input features

To select the best model architecture

To convert categorical variables into a binary format that can be used by machine learning algorithms

To reduce the dimensionality of the input data

To prevent overfitting

To optimize the model's hyperparameters

Min-max scaling

Standardization (z-score)

Both A and B

None of the above

To ensure that all input features have a similar scale, so that the model can learn more effectively

To reduce the dimensionality of the input data

To prevent overfitting

To optimize the model's hyperparameters

To measure the performance of a model by comparing true and predicted class labels

To identify the most important input features

To optimize the model's hyperparameters

To reduce the complexity of the model

Random forests

Support vector machines

Linear regression

K-means clustering

A technique that combines multiple models to make a single prediction

A method for regularizing models to prevent overfitting

A technique for splitting data into training and testing sets

A method for optimizing hyperparameters

Decision trees

Support vector machines

Linear regression

K-nearest neighbors

Decision trees

Support vector machines

Linear regression

K-nearest neighbors

Decision trees

Support vector machines

K-nearest neighbors

Neural networks

The number of input features

The use of labeled data

The assumption of a fixed functional form for the underlying relationship between input and output variables

The use of regularization techniques

Decision trees

Support vector machines

K-nearest neighbors

Neural networks

To train the model

To test the model's performance on new, unseen data

To tune the model's hyperparameters and select the best model

To preprocess the input data

Dropout

L1 regularization

L2 regularization

Both B and C

To reduce the complexity of the model and prevent overfitting

To increase the complexity of the model and improve performance

To optimize the model's hyperparameters

To identify the most important input features

Mean squared error

Cross-entropy loss

Huber loss

Hinge loss

Mean squared error

Cross-entropy loss

Huber loss

Hinge loss

To identify input features

To measure the error between predicted and actual outputs

To optimize hyperparameters

To find the best model architecture

K-means clustering

Apriori

Decision tree

t-SNE

k-means clustering

Principal Component Analysis (PCA)

k-fold cross-validation

Random sampling

A model that is too complex and has learned the noise in the training data

A model that performs well on new, unseen data

A model that is too simple and does not capture the underlying patterns in the data

A model that performs well on both training and testing data

A type of unsupervised learning

A type of deep learning

A type of machine learning where the model is trained on labeled data

A type of reinforcement learning

A model that performs poorly on training data

A model that performs poorly on new, unseen data

A model that performs well on both training and testing data

A model that is too complex and has learned the noise in the training data

A variable used to split the data

A type of algorithm used for learning

The target or output variable associated with an instance

An error metric used to evaluate model performance

To learn the best possible mapping from inputs to outputs

To find hidden patterns in the data

To optimize the rewards in a given environment

To compress the data into a lower-dimensional space

Clustering and regression

Regression and classification

Dimensionality reduction and clustering

Anomaly detection and dimensionality reduction

Clustering

Dimensionality reduction

Regression

Anomaly detection