Quiz 1

Question 1: What is the primary goal of data preprocessing in data science?

• A) Displaying raw data to stakeholders.

• B) Increasing the complexity of the dataset.

• C) Cleaning and transforming data for analysis.

• D) Collecting data from various sources without any modification.

Which algorithm is commonly used for classification tasks in supervised machine learning?

• A) K-Means

• B) Hierarchical Clustering

• C) Random Forest

• D) Principal Component Analysis (PCA)

: What does the term "overfitting" mean in machine learning?

• A) The model's inability to make any predictions.

• B) A situation where the model performs well on unseen data.

• C) The ideal outcome of the training process.

• D) The model learning the training data too closely, leading to poor performance on new data.

In the context of neural networks, what does the term "epoch" refer to?

• A) A layer in a neural network.

• B) One complete pass through the entire training dataset.

• C) The activation function of a neuron.

• D) The loss function used in the network.

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

• A) The model's inability to make any predictions.

• B) A situation where the model performs well on unseen data.

• C) The ideal outcome of the training process.

• D) The model learning the training data too closely, leading to poor performance on new data.

What is the purpose of a confusion matrix in classification tasks?

• To confuse the model during training.

• B) To visualize data distributions.

• C) To evaluate the performance of a classification model.

• D) To preprocess data before analysis.

Which of the following is NOT a step in the CRISP-DM data mining process?

• A) Data Understanding

• B) Business Understanding

• C) Data Deletion

• D) Deployment

What is the main advantage of using regularization techniques in machine learning models?

• A) Increased complexity and accuracy.

• B) Faster training times.

• C) Prevention of overfitting.

• D) Elimination of bias in the model.

What is the purpose of the term "gradient" in gradient descent optimization?

• A) It refers to the number of layers in a neural network.

• B) It's the name of a specific machine learning algorithm.

• C) It indicates the direction of steepest increase in a function.

• D) It represents the final output of a machine learning model.

What does the term "bias-variance trade-off" refer to in machine learning?

• A) The trade-off between CPU and GPU usage during training.

• B) The balance between a model's ability to capture complex patterns and its sensitivity to noise.

• C) The trade-off between linear and nonlinear models.

• D) The balance between training time and model accuracy.