Data Science Quiz (original) (raw)

What is the purpose of the term "feature engineering" in machine learning?

• Extracting valuable information from the target variable
• Creating new features or modifying existing ones to improve model performance
• Selecting the most important features for model training
• Normalizing feature values to have zero mean and unit variance

In machine learning, what is feature scaling?

• Modifying features to have comparable scales
• Creating new features from existing ones
• Removing irrelevant features from the dataset
• Encoding categorical variables

What is the primary purpose of the term "word embedding" in natural language processing (NLP)?

• Representing words as sparse binary vectors
• Encoding words into numerical vectors with continuous values
• Tokenizing sentences into individual words
• Reducing the dimensionality of word representations

In statistics, what does the term "p-value" represent in hypothesis testing?

• The probability of making a Type II error
• The probability of observing the data given that the null hypothesis is true
• The significance level for the test
• The probability of rejecting the null hypothesis

Explain the concept of the "bias-variance trade-off" in machine learning.

• The trade-off between the number of features and model complexity
• Balancing precision and recall in classification problems
• The trade-off between model flexibility and stability
• Minimizing both training and testing errors

What is the purpose of the term "Bayesian inference" in statistics and machine learning?

• Estimating parameters based on prior knowledge and observed data
• Fitting models to the training data using maximum likelihood estimation
• Combining predictions from multiple models using Bayesian averaging
• Evaluating models using cross-validation

What is the role of the "learning rate" in gradient descent optimization?

• The size of the steps taken during each iteration
• The regularization strength applied to the mod
• The number of iterations in the optimization process
• The speed at which the algorithm converges

Explain the term "Gini impurity" in the context of decision trees.

• A measure of impurity or disorder in a set of data
• A measure of information gain in feature selection
• A criterion used to split nodes in a decision tree
• A method for pruning decision trees

What is the role of the term "dropout" in neural networks?

• Improving model interpretability
• Reducing the learning rate during training
• Introducing non-linearity to the model
• Preventing overfitting by randomly dropping neurons during training

Explain the term "precision" in the context of binary classification.

• The ratio of true positive predictions to the total positive predictions
• The ratio of true positive predictions to the sum of true positives and false negatives
• The ratio of true positive predictions to the sum of true positives and false positives
• The ratio of true positive predictions to the total predictions made by the model

There are 26 questions to complete.

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