Machine Learning - Dr.Rami

In the context of machine learning, what is 'overfitting'?

• 1. When a model performs poorly on the training data 

• 2. When a model is too complex and captures noise in the training data 

• 3. When a model is too simple to capture patterns in the data 

• 4. When a model takes too long to train 

What is the main goal of unsupervised learning?

• 1. To predict the outcome for new data points 

• 2. To classify data based on labels 

• 3. To discover hidden patterns in data without labels 

• 4. To reduce the dimensionality of data 

What does 'cross-validation' in machine learning refer to?

• 1. A method for comparing different models 

• 2. A technique for evaluating a model by partitioning the data 

• 3. A process for combining different algorithms 

• 4. A strategy for selecting features in a model 

In machine learning, what is 'feature selection' primarily used for?

• 1. To choose the type of model to use 

• 2. To identify the most important variables in the dataset 

• 3. To determine the number of clusters in the data 

• 4. To set the parameters for the learning algorithm 

What is a 'neural network' in machine learning?

• 1. A data storage system 

• 2. An algorithm inspired by the structure and function of the human brain 

• 3. A method for cleaning data 

• 4. A type of computer hardware 

Which of these is a common metric for evaluating the performance of a regression model?

• 1. Accuracy 

• 2. Precision and Recall 

• 3. Mean Squared Error 

• 4. F1 Score 

Why is data preprocessing important in machine learning?

• 1. To increase the amount of data 

• 2. To enhance the visual appeal of data 

• 3. To ensure the data is in a suitable format for analysis 

• 4. To make the learning algorithm run faster 

What is 'bias' in the context of machine learning?

• 1. The model's tendency to consistently learn the wrong thing 

• 2. The amount of data required to train the model 

• 3. The model's ability to perform well on unseen data 

• 4. The complexity of the model 

Which technique is used to handle imbalanced datasets in supervised learning?

• 1. Dimensionality reduction 

• 2. Data augmentation 

• 3. Regularization 

• 4. Feature scaling 

What does the term 'ensemble learning' refer to in machine learning?

• 1. Learning with a large dataset 

• 2. Learning using a single, highly complex model 

• 3. Combining multiple machine learning models to improve performance 

• 4. Learning from a dataset with diverse features