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.
It's difficult to find linear predictor function in a sentence. 用linear predictor function造句挺难的

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.