Contents
What is partial dependence?
A partial dependence (PD) plot depicts the functional relationship between a small number of input variables and predictions. They show how the predictions partially depend on values of the input variables of interest. For example, a PD plot can show whether the probability of flu increases linearly with fever.
What is partial dependence in machine learning?
The partial dependence plot (short PDP or PD plot) shows the marginal effect one or two features have on the predicted outcome of a machine learning model (J. H. Friedman 200129). A partial dependence plot can show whether the relationship between the target and a feature is linear, monotonic or more complex.
What is y-axis in partial dependence plot?
The y-axis of a partial dependence plot for regression represents the marginal impact of the independent variable to the dependent variable. E.g. if the line is at 0, then for that value of the independent variable, there is 0 impact to the dependent variable.
What is partial dependency normal form linked to?
Partial dependency is the dependency based only on a part of a composite primary key . So, in first normal form (1 NF) when we have composite primary key it will be assoicated with partial dependencies . Therefore we can say (1 NF ) is associated with partial dependencies .
When is the partial dependence function modeled separately?
When a feature is categorical, rather than continuous, the partial dependence function is modeled separately for all of the K different classes of said feature. It maps the predictions for each respective class at given feature values of xS x S (Hastie, Tibshirani, and Friedman 2013).
What are the disadvantages of a partial dependence plot?
An assumption of the PDP is that the features in C are not correlated with the features in S. If this assumption is violated, the averages calculated for the partial dependence plot will include data points that are very unlikely or even impossible (see disadvantages).
How is a partial dependence generated in sklearn?
The values at which the partial dependence should be evaluated are directly generated from X. For 2-way partial dependence, a 2D-grid of values is generated. The values field returned by sklearn.inspection.partial_dependence gives the actual values used in the grid for each input feature of interest. They also correspond to the axis of the plots.
How is partial dependence related to target response?
Intuitively, we can interpret the partial dependence as the expected target response as a function of the input features of interest. Due to the limits of human perception the size of the set of input feature of interest must be small (usually, one or two) thus the input features of interest are usually chosen among the most important features.