In this blog I’ll share a few plotting functions I regularly use.
I originally wrote this function to plot scatter plots of two variables plus a linearly fitted line.
But later I realized seaborn
already has regplot
and lmplot
that do the same thing but better.
You can read more about them in this tutorial (and learn not to re-invent the wheel!).
I have repurposed it to visualize model evaluation.
When your y
is a continuous variable, you can plot the predicted y
values on one axis, and the actual y
values on another, and see how well they line up along y=x
. To plot the y=x
line, simply set slope=1
, y_intercept=0
.
Here is an example:
This is a wrapper function for seaborn
’s heatmap
. I use it to look at the correlations between variables in a pandas DataFrame, which is quite useful during exploratory data analysis.
Here is an example:
When you see highly correlated variables, you have to be very careful if you are using a linear regression model (the LINE assumptions!). Plotting a heatmap is a quick way to check for that.