Precision-recall and ROC curves

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En provenance du cours de University of Michigan

Applied Machine Learning in Python

3216 notes

University of Michigan

Applied Machine Learning in Python

3216 notes

Cours 3 sur 5 dans Specialization Applied Data Science with Python

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

À partir de la leçon

Module 3: Evaluation

This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.

Model Evaluation & Selection22:14

Confusion Matrices & Basic Evaluation Metrics12:05

Classifier Decision Functions7:21

Precision-recall and ROC curves6:32

Multi-Class Evaluation13:06

Regression Evaluation6:08

Model Selection: Optimizing Classifiers for Different Evaluation Metrics13:16

Rencontrer les enseignants

Kevyn Collins-Thompson
Associate Professor
School of Information

Precision-Recall Curves are very widely used evaluation method from machine

learning.

As we just saw in example, the x axis shows precision and

the y axis shows recall.

Now an ideal classifier would be able to achieve perfect precision of 1.0 and

perfect recall of 1.0.

So the optimal point would be up here in the top right.

And in general, with precision recall curves, the closer in some sense,

the curve is to the top right corner, the more preferable it is,

the more beneficial the tradeoff it gives between precision and recall.

And we saw some examples already of how there is a tradeoff between those two

quantities, between precision and recall, with many classifiers.

This example here is an actual precision recall curve that we generated

using the following notebook code.

The red circle indicates the precision and

recall that's achieved when the decision threshold is zero.

So I created this curve using exactly the same method

as we saw in the previous example, by

looking at the decision function output from a support vector classifier.

Applying very end decision boundary,

looking at how the precision of recall change as the decision boundary changed.

Fortunately, learn has a function that's built in

that does all of that, that could compute the precision of recall curve.

And that's what we've been using in the notebook here.

So you can see that in this particular application

there is a general downward trend.

So as the precision of the classifier goes up, the recall tends to go down.

In this particular case you'll see also that It's not exactly a smooth curve.

There are some jaggy errors and, in fact,

the jumps tend to get a little bigger as we approach maximum precision.

This is a consequence of how the formulas for precision and recall are computed.

They use discrete counts that include the number of true positives.

And so as the decision threshold increases, there are fewer and

fewer points that remain as positive predictions.

So the fractions that are computed for these smaller numbers can

change pretty dramatically with small changes in the decision threshold.

And that's why these sort of trailing edges of

the Precision-recall curve can appear a bit jagged when you plot them.

ROC curves or receiver operating characteristic curves are a very widely

used visualization method that illustrate the performance of a binary classifier.

ROC curves on the X-axis show a classifier's False Positive Rate so

that would go from 0 to 1.0, and on the Y-axis they show

a classifier's True Positive Rate so that will also go from 0 to 1.0.

The ideal point in ROC space is one where the classifier achieves

zero, a false positive rate of zero, and a true positive rate of one.

So that would be the upper left corner.

So curves in ROC space represent different tradeoffs as

the decision boundary, the decision threshold is varied for the classifier.

So just as in the precision recall case, as we vary decision threshold,

we'll get different numbers of false positives and

true positives that we can plot on a chart.

The dotted line here that I'm showing is the classifier

curve that secretly results from a classifier that randomly guesses

the label for a binary class.

It's basically like flipping a coin.

If you have two classes with equal numbers of positive and negative incidences, then

flipping a coin will get you randomly equal numbers of false positives and

true positives for a large virus data sets.

So the dotted line here is used as a base line.

So bad classifier will have performance that is random or

maybe even worse than random or be slightly better than random.

Reasonably good classifier will give an ROC curve that is consistently better than

random across all decision threshold choices.

And then an excellent classifier would be one like I've shown here,

which is way up into the left.

This particular example is an example of a logistic regression classifier using

the notebook example you've seen.

So, the shape of the curve can be important as well,

the steepness of the curve, we want classifiers that

maximize the true positive rate while minimizing the false positive rate.

Now as we'll see next, we can qualify the goodness of a classifier

in some sense by looking at how much area there is underneath the curve.

So the area underneath the random classifier

is going to be 0.5 but then the area,

as you can see, the size of

the bumpiness of the classifier as it approaches the top left corner.

Well, the area underneath the curve will get larger and larger.

It will approach 1.

And so, as we'll see in the next slide.

We use something called area under the curve, AUC.

That's the single number that measures this total area underneath the ROC curve

as a way to summarize a classifier's performance.

So, an AUC of zero represents a very bad classifier, and

an AUC of one will represent an optimal classifier.

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