Introduction to Machine Learning Quiz

What is the primary goal of supervised learning in machine learning?

• To find patterns in data without any labels

• To predict the output for new data based on labeled training data

• To reduce the dimensionality of the data

• To cluster similar data points together

Which of the following is an example of a classification algorithm?

• Linear Regression

• K-Means Clustering

• Decision Tree

• Principal Component Analysis

In the context of machine learning, what does the term "overfitting" refer to?

• A model that performs well on training data but poorly on new, unseen data

• A model that performs well on both training and test data

• A model that is too simple to capture the underlying trend of the data

• A model that has a high bias and low variance

Which of the following is a common method to prevent overfitting in machine learning models?

• Increasing the complexity of the model

• Using a smaller training dataset

• Applying regularization techniques

• Ignoring the test dataset

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

• To initialize the weights of the network

• To introduce non-linearity into the network

• To normalize the input data

• To reduce the dimensionality of the data

Which of the following is a loss function commonly used for regression tasks?

• Cross-Entropy Loss

• Hinge Loss

• Mean Squared Error

• Log Loss

In the context of machine learning, what does the term "feature scaling" refer to?

• The process of selecting the most important features

• The process of normalizing the range of independent variables

• The process of increasing the number of features

• The process of reducing the number of features

Which of the following algorithms is used for unsupervised learning?

• Support Vector Machine

• Naive Bayes

• K-Means Clustering

• Random Forest

What is the purpose of the learning rate in gradient descent optimization?

• To determine the number of iterations

• To control the step size during the update of weights

• To decide the number of layers in a neural network

• To set the initial values of the weights

Which of the following is a technique used to handle missing data in a dataset?

• Data Augmentation

• Imputation

• Feature Extraction

• Hyperparameter Tuning