|Adult Census Income Binary Classification Dataset (Visualize)|
|Adult Census Income Binary Classification Dataset (Visualize) (Income)|
|Precision/Recall Experiment View|
On the left side of the visualization is the Lift Curve. This curve is designed to what proportion of the sample you would have to run through in order to find a certain number of true positives. Effectively, this tells you how "efficient" your model is. A more "efficient" model can find the same number of true positives (aka successes) from a smaller sample. In our case, this would mean that we would have to contact less people in order to find X number of people with "Income > 50k". For analytic purposes, we are looking for curves that are closer to the top left corner. We see that the more "efficient" model is the Boosted Decision Tree. Let's take a look at the curves from the other "Evaluate Model" module.
|Precision/Recall Curve 2|
|Lift Curve 2|
|Precision/Recall Curve (Final)|
|Lift Curve (Final)|