Quizathon

What is the primary goal of supervised learning?

• To find hidden patterns and structures in the data
• To predict the output variable based on input variables and labeled examples
• To cluster data points into groups based on their similarities.
• To optimize a given objective function using gradient descent.

Which machine learning algorithm is commonly used for classification tasks and is based on the concept of "nearest neighbors"?

• Decision Trees
• K-Nearest Neighbors (KNN)
• Support Vector Machines
• Naive Bayes

What does the term "overfitting" mean in the context of machine learning?

• The model performs well on unseen data.
• The model generalizes well to new, unseen data.
• The model captures noise and random fluctuations instead of the underlying pattern.
• The model has a high bias and does not fit the training data well.

Which of the following evaluation metrics is commonly used for imbalanced datasets in binary classification?

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Receiver Operating Characteristic (ROC) Area Under the Curve (AUC-ROC)
• R-squared

Which machine learning technique is used for reducing the dimensionality of data while preserving its important features?

• Clustering
• Regression
• Feature Engineering
• Principal Component Analysis (PCA)

The bias variance tradeoff refers to

• Balancing the bias and variance of a model to improve accuracy.
• The tradeoff between model complexity and interpretability.
• A technique to handle missing values in the dataset.
• The tradeoff between overfitting and underfitting in machine learning models.

Which deep learning architecture is commonly used for image recognition tasks and consists of multiple convolutional and pooling layers?

• Long Short-Term Memory (LSTM) networks
• Feedforward Neural Networks
• Recurrent Neural Networks (RNN)
• Convolutional Neural Networks (CNN)

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

• To compute the cost function during training
• To normalize the input data before passing it to the next layer.
• To introduce non-linearity to the model.
• To regulate the learning rate during gradient descent.

In reinforcement learning, what does the agent do to learn and improve its performance

• It relies solely on labeled examples to adjust its parameters.
• It explores the environment, takes actions, and receives feedback in the form of rewards or penalties.
• It uses statistical methods to analyze the data and make predictions.
• It learns from its past mistakes to avoid making them again in the future.

What is the primary purpose of the "dropout" technique in neural networks?

• To prevent overfitting by randomly dropping some neurons during training.
• To improve the convergence rate during gradient descent.
• To reduce the computational complexity of the model.
• To add noise to the input data for better generalization.