linear predictor function造句

• This example shows that a linear predictor function can actually be much more powerful than it first appears : It only really needs to be linear in the " coefficients ".
• An example of the usage of such a linear predictor function is in linear regression, where each data point is associated with a continuous outcome " y " " i ", and the relationship written
• This formulation expresses logistic regression as a type of generalized linear model, which predicts variables with various types of probability distributions by fitting a linear predictor function of the above form to some sort of arbitrary transformation of the expected value of the variable.
• The idea behind all of them, as in many other statistical classification techniques, is to construct a linear predictor function that constructs a score from a set of weights that are linearly combined with the explanatory variables ( features ) of a given observation using a dot product:
• This type of nearest neighbor method for prediction is often considered diametrically opposed to the type of prediction used in standard linear regression : But in fact, the transformations that can be applied to the explanatory variables in a linear predictor function are so powerful that even the nearest neighbor method can be implemented as a type of linear regression.
• In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are affine function of " X "; less commonly, the median or some other quantile of the conditional distribution of " y " given " X " is expressed as a linear function of " X ".
• An example is polynomial regression, which uses a linear predictor function to fit an arbitrary degree polynomial relationship ( up to a given order ) between two sets of data points ( i . e . a single real-valued explanatory variable and a related real-valued dependent variable ), by adding multiple explanatory variables corresponding to various powers of the existing explanatory variable.