Then I use np.meshgrid to get the data in the right shape for the contour plot. First, I generate data over a regular grid to illustrate different claim amounts and then probabilities.
#Contour plot in python code
The code snippet is small enough to just copy-paste entirely.
So I have been making the subsequent filled contour plot I am going to show in the next section to illustrate this. In this framework, it is more important to audit a high dollar claim than a lower dollar claim, even if the higher dollar value claim has a lower probability. I am working with medical insurance claims data at HMS, and often determining models to audit those claims in some way. (If you have a vector of varying probabilities, in R code the estimated revenue will then look like prob 0.02 sum( (50*prob - 1)*pover ).)īut many of the decisions I work with are not a single number in the benefits column. Any single advert may be a bust, but if your model is right and you send out a bunch, you should make this much money in the end. The probabilities you get from your predictive model can be thought of as in the long run averages. So if you have a probability of 10% for 2000 customers, you would expect to make 2000 * (50*0.1 - 1) = 8000. So in this case you need the predicted probability to be above 2% to have an expected positive return on the investment of sending the advert. In this framework, if you have a predictive model for the probability the advert will be successful, then your decision threshold will look like this: $50*probability - $1 If the person buys the product, your company makes $50, and the advert only costs $1 to send. For example, say you are sending adverts in the mail for a product. If you can identify the costs and benefits of making particular decisions, you can set a simple threshold to make that decision. do I do some process to this observation if the probability is 20%, 30%, 60%, etc. So people often talk about setting a decision threshold to turn a predicted probability into a binary yes/no decision. Let's take another example for contour plot.I’ve been making a chart that looks similar to this for a few different projects at work, so figured a quick blog post to show the notes of it would be useful.
In this example we will plot contour lines with the help of contour() function: Let us cover some Examples of this function. This parameter is used to determine the number and position of the contour lines. This parameter indicates the height values over which the contour is drawn. This parameter indicates 2-D NumPy arrays with having same shape as Z or same like 1-D arrays in a manner such that len(X)=M and len(Y)=N (where M are rows and N are columns of Z) Let us discuss the parameters of this function: The required syntax for the same is given below: ( Z,, **kwargs) The function () is useful when Z = f(X, Y) here, Z changes as a function of input X and Y.Ī contourf() function is also available in matplotlib which allows us to draw filled contours. To visualize the meteorological department. There are some uses of contour plot given below: There are two functions in Matplotlib that is contour()(this is used to draw the contour lines) and contourf()(this is used to draw filled contours). We should use contour plot if you want to see how the value of Z changes as a function of two inputs that is X and Y, like this Z = f(X,Y).Ī contour line or isoline of a function of two variables is basically a curve along which the function has a constant value. Suppose there are two variables X and Y and you want to plot them then the response of two variables will be Z and it will be plotted as slices on the X-Y plane due to which contours are also referred to as Z-slices or iso-response. Contour plots are also known as Level Plots.
To create a visualization of 3-Dimensional plots in 2-Dimensional space we use contour plots in Matplotlib.
#Contour plot in python how to
In this tutorial, we will cover what is a contour plot and how to create contour plots in Matplotlib.
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