Machine Learning and Data Science Quiz

Which technique in machine learning is used to group unlabelled data points into clusters?

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Semi-supervised learning

Which statistical method is used for making predictions based on historical time series data?

• Regression analysis

• K-means clustering

• Decision trees

• Fourier transform

Which algorithm is often used for classification problems when the data is linearly separable?

• K-nearest neighbors

• Decision trees

• Support Vector Machines (SVM)

• Random Forest

What is the purpose of the 'test set' in machine learning?

• To train the model

• To fine-tune hyperparameters

• To evaluate the model's performance on unseen data

• To provide additional features to the model

Which technique is used to address missing values in a dataset by replacing them with predicted values?

• Mean imputation

• Mode imputation

• K-nearest neighbors imputation

• Regression imputation

Which technique is commonly used for reducing overfitting in machine learning models?

• Dropout

• Feature scaling

• Gradient boosting

• Principal component analysis

Which algorithm is often used for clustering in unsupervised learning?

• Decision Trees

• K-Means

• Support Vector Machines

• Random Forest

What is the difference between unsupervised learning and semi-supervised learning?

• Unsupervised learning uses only labeled data, while semi-supervised learning uses both labeled and unlabeled data.

• Unsupervised learning uses only unlabeled data, while semi-supervised learning uses both labeled and unlabeled data.

• Unsupervised learning does not use any data, while semi-supervised learning uses labeled data only.

• Unsupervised learning and semi-supervised learning are the same

What is the role of a loss function in the training of a machine learning model?

• To measure the accuracy of the model's predictions

• To optimize the model's parameters during training

• To generate synthetic data for training

• To prevent overfitting

What is the purpose of the activation function in a neural network?

• To compute the gradient of the loss function

• To introduce non-linearity to the model

• To reduce overfitting

• To normalize the input data