Learning For Everyonea) Vector of features
b) Scalar points
c) Polynomials
d) Clusters
The variables are converted into a vector of features, and then given as an input to the algorithm. The vector is of the size (number of features x number of training data sets). The output of the learner is usually given as a polynomial.
a) Testing Data
b) Label Data
c) Training Data
d) Cross-Validation Data
The learner gets access to a particular set of data on which it trains. This data is called as training data. Testing Data is used for testing of the learner’s outputs. The best outputs are then used on the cross-validation data. The label data is a representation of different types of the dependent variables.
a) domain set
b) training set
c) label set
d) test set
Label Set denotes all the possible forms the target variable can take (for e.g. {0,1} or {yes, no} in a logistic regression problem). Domain Set represents the vector of features, given as input to the learner. Training Set and Test Set are parts of the Domain Set which are used for training and testing respectively.
a) Predictor, or Hypothesis, or Classifier
b) Predictor, or Hypothesis, or Trainer
c) Predictor, or Trainer, or Classifier
d) Trainer, or Hypothesis, or Classifier
The output is called a predictor when it is used to predict the type or the numerical value of the target variable. It is called a hypothesis when it is a general statement about the data set. It is called a classifier when it is used to classify the training set in two or more types.
a) True
b) False
The learner has no prior knowledge about the distribution. It is assumed that the distribution is completely arbitrary. It is also assumed that there is a function which “correctly” labels the training examples. The learner’s job is to find out this function.
Items per page: