Entry 27: Figuring out openml.org
Figuring out how to get the data from openml.org into the Entry 26e notebook (MNIST was surprisingly difficult. All the datasets are saved as arff files, an ...
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Entry 26: Setting thresholds - precision, recall, and ROC
I don’t always want the default threshold for determining the classification (negative or positive) the way I did in Entry 24. As discussed in the precision ...
Entry 25: Baseline Models
As discussed in Entry 16, certain characteristics in the data can make a model look like it’s performing better than it is. One of these characteristics is c...
Entry 24: Scoring Classification Models - Implementation
Now that I’ve got a handle on the measurement options and equations for classification problems, it’s time to implement those measures on actual models.
Entry 23: Scoring Classification Models - Theory
Classification models present a different challenge than regression models. Because a numeric value isn’t returned, another way of measuring goodness of fit ...