# Visualizing Model Input Effects

2018-06-14

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. It can show whether high energy level will decrease the probability of having flu. PD can also show the type of relationship, such as a step function, curvilinear, linear and so on.

The simplest PD plots are 1-way plots, which show how a model’s predictions depend on a single input. The plot below shows the relationship (according the model that we trained) between price (target) and number of bathrooms. Here, we see that house prices increase as we increase the number of bathroom up to 4. After that it does not change the house price.

These types of plots are helpful for understanding the mechanics behind a model.

## Linear Programming in Python

2019-05-24

Francisco Alvarez shows us an example of linear programming in Python: The first two constraints, x1 ≥ 0 and x2 ≥ 0 are called nonnegativity constraints. The other constraints are then called the main constraints. The function to be maximized (or minimized) is called the objective function. Here, the objective function is x1 + x2. Two classes of […]

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## Exploratory Data Analysis with inspectdf

2019-05-23

Laura Ellis continues a dive into Exploratory Data Analysis, this time using the inspectdf package: I like this package because it’s got a lot of functionality and it’s incredibly straightforward to use. In short, it allows you to understand and visualize column types, sizes, values, value imbalance & distributions as well as correlations. Better yet, […]

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June 2018
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