AI Bootcamp: 2024 Edition - Session 3

What is the primary purpose of regression analysis?

• To predict outcomes based on input variables

• To classify data into categories

• To visualize data trends

• To perform hypothesis testing

In the equation y = w1x + w2, what do the parameters w1 and w2 represent?

• They are the coefficients of the linear equation

• They are the independent variables

• They are the predicted values

• They are the error terms

What does the term 'w1' signify in the context of linear regression?

• The slope of the regression line

• The intercept of the regression line

• The dependent variable

• The residual error

What is the output range of the sigmoid function?

• 0 to 1

• -1 to 1

• 0 to infinity

• -infinity to infinity

In which type of machine learning model is the sigmoid function commonly used?

• Clustering models

• Regression models

• Classification models

• Dimensionality reduction models

Which type of machine learning is primarily concerned with finding patterns in data without labeled outcomes?

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Semi-supervised learning

What is a key characteristic of reinforcement learning?

• It requires labeled data for training

• It learns through trial and error

• It focuses on clustering data

• It is used for dimensionality reduction

What is the purpose of using StandardScaler in scikit-learn?

• To normalize the data to a range of [0, 1]

• To standardize features by removing the mean and scaling to unit variance

• To reduce the dimensionality of the dataset

• To encode categorical variables into numerical values

What is the effect of using OneHotEncoder on categorical variables?

• It converts categorical variables into a binary matrix

• It replaces missing values with the mean

• It scales the features to a standard normal distribution

• It reduces the number of categories by merging similar ones

What does the term 'feature engineering' refer to in machine learning?

• Creating new features from existing data

• Reducing the number of features in a dataset

• Visualizing data for better understanding

• Encoding categorical variables into numerical values