Machine Learning Basics
When a response is not given.
What are unsupervised methods?
The null hypothesis for coefficients in linear and logstic regression.
What is Bi <> 0?
Is minimized during cluster assignments in K-means.
What is within-cluster variation?
Measures the quality of fit or accuracy in regression models.
What is the Mean Squared Error?
Primary purpose of a loss function in machine learning.
What is measuring the error between predicted and actual values?
The model is too simple to capture patterns.
What is bias?
The probability when b0 = 1, b1 = -2, and xi = 0.5.
What is the p(y=1|0.5) = 0.5?
Occurs after observations are assigned to a cluster in K-means.
What is calculating the centroid?
Inherent noise or variability in data that cannot be reduced.
What is irreducible error?
Role of the regularization parameter λ.
What is adjusting the strength of the regularization?
The reencoding of input data in a neural network.
What is a representation?
Minimized when estimating parameters in linear regression.
What is sum squares of the residual?
Results in more balanced clusters?
What is complete linkage?
Primary goal of using bagging in machine learning.
What is decreasing variance?
Smaller learning rates in gradient descent reduce the risk of?
What is avoiding overshooting minimum?
When Xi and Yi are observed.
What is supervised learning?
Does not require the specification of the number of clusters.
What is hierarchical clustering?
Addresses correlated trees in bagging by introducing randomness in feature selection?
What is Random Forest?
A change in distribution between training and testing data?
What is covariate shift?
The theoretical floor of classifier performance.
What is the Bayes Optimal Classifier?
The ratio that defines R2.
What is the SSR/SST or 1 - SSE/SST?
Done to avoid local minima in k-means clustering.
What is running with multiple initializations?
Observed relationship between True Positive Rate and and the False Positive Rate.
What is a positive correlation?
A cause of sparsely populated space?
What is the Curse of Dimensionality?