CLASS TEST1

What is overfitting in machine learning?

• Overfitting is when a model performs equally well on both training and unseen data.

• Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

• Overfitting refers to a model that is trained on too little data, leading to poor performance.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.

What is underfitting?

• Underfitting is when a model perfectly fits the training data.

• Underfitting happens when a model has too many parameters.

• Underfitting is when a model is too simplistic to learn from the data.

• Underfitting occurs when a model is overly complex for the data.

What is a decision tree?

• A decision tree is a model used for classification and regression that splits data into branches to make decisions.

• A decision tree is a clustering algorithm.

• A decision tree is a linear regression model.

• A decision tree is a type of neural network.

What is cross-validation?

• A way to optimize the performance of a single model.

• Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.

• A technique for visualizing data distributions.

• A method for increasing the size of a dataset.

What is the difference between classification and regression?

• Classification requires more data than regression.

• Classification predicts numerical values; regression predicts categories.

• Classification is used for time series; regression is for image analysis.

• Classification predicts categories; regression predicts continuous values.

What is the role of a validation set in machine learning?

• A validation set is used to visualize the data.

• A validation set is used to train the model.

• A validation set is the same as the test set.

• A validation set helps in tuning the model's hyperparameters and preventing overfitting.

What is the role of an optimizer in machine learning?

• An optimizer is a method for data augmentation.

• An optimizer selects the features for model training.

• An optimizer is used to visualize the training process.

• An optimizer adjusts the weights of a model to minimize the loss function.

What is the difference between supervised and unsupervised learning?

• There is no difference; they are the same.

• Unsupervised learning is a type of supervised learning.

• Supervised learning uses labeled data, while unsupervised learning does not.

• Supervised learning is faster than unsupervised learning.

What is the purpose of a loss function in machine learning?

• A loss function is only relevant for unsupervised learning.

• A loss function is a method for feature selection.

• A loss function measures how well a model's predictions match the actual outcomes.

• A loss function is used to visualize data.

Which of the following algorithms is commonly used in supervised learning?

• Support Vector Machines (SVM)

• Hierarchical clustering

• Principal Component Analysis (PCA)

• K-means clustering