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.
Model Selection: Optimizing Classifiers for Different Evaluation Metrics
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Applied Machine Learning in Python
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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.
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Kevyn Collins-ThompsonAssociate Professor
School of Information
Now that you've seen a number of different evaluation metrics
for both binary and multiclass classification,
let's take a look at how you can apply them as criteria for
selecting the best classifier for your application,
otherwise known as model selection.
In previous lectures we've seen a number of
different evaluation frameworks for potential model selection.
First, we simply did training and testing on the same dataset,
which as we well know,
typically overfits badly and doesn't generalize well to new data.
As a side note however,
it can serve as a useful sanity check to make sure
your software engineering and feature generation pipeline is working correctly.
Second, we frequently use
the single train-test split to produce a single evaluation metric.
While fast and easy,
this doesn't give as realistic a set of
estimates for how well the model may work on future, new data.
And we don't get a good picture for the variance in the evaluation metrics that may
result as we do prediction on different test sets.
Third, we used k-fold cross-validation to create K random train-test splits,
where the evaluation metric was averaged across splits.
This leads to models that are more reliable on unseen data.
In particular, we can also use grid search using for
example the GridSearchCV method within each cross-validation fold,
to find optimal parameters for a model with respect to the evaluation metric.
The default evaluation metric used for a cross-val score or GridSearchCV is accuracy.
So how do you apply the new metrics you've learned about here like
AUC in model selection?
Scikit-learn makes this very easy.
You simply add a scoring parameter that's set to
the string with the name of the evaluation metric you want to use.
Let's first look at an example using the scoring parameter for cross-validation,
and then we'll take a look at the other primary method of model selection, grid search.
In the notebook here we have a cross-validation example where we're running
five folds using a support vector classifier
with a linear kernel and C parameter set to one.
The first call to cross-val score just uses default accuracy as the evaluation metric.
The second call uses the scoring parameter using the string 'roc_auc',
and this will use AUC as the evaluation metric.
The third call sets the scoring parameter to 'recall',
to use that as the evaluation metric.
You can see the resulting list of five evaluation values,
one per fold for each metric.
Now, here we're not doing any parameter tuning we're simply evaluating
our model's average performance across multiple folds.
Now, in this grid search example we use
a support vector classifier that uses a radio basis function kernel.
And the critical parameter here is the gamma parameter that
intuitively sets the radius or width of influence of the kernel.
We use GridSearchCV to find the value of gamma
that optimizes a given evaluation metric in two cases.
In the first case, we just optimize for average accuracy;
in the second case we optimize for AUC.
In this particular case the optimal value of gamma happens to be the same,
point zero-zero-one, for both evaluation metrics.
As we'll see later in other cases,
the optimal parameter value can be quite
different depending on the evaluation metric used to optimize.
You can see the complete list of names for
the evaluation metric supported by the scoring parameter by
running the following code that uses the score's variable imported from sklearn metrics.
You can see metrics for classification such as the
string 'precision_ micro' that represents
micro-averaged precision as well as metrics for regression such as
the R2 metric for R-squared regression loss.
Let's take a look at a specific example that shows
how a classifier's decision boundary changes
when it's optimized for different evaluation metrics.
This classification problem is based on
the same binary digit classifier training and
test sets we've been using as an example throughout the notebook.
In these classification visualization examples, the positive examples,
the digit one are shown as black points and the region of
positive class prediction is shown in
the light-colored or yellow region to the right of this decision boundary.
The negative examples, all other digits,
are shown as white points.
And the region of negative class prediction here in
these figures is to the left of the decision boundary.
The data points have been plotted using two out of
the 64 future values in the digits' dataset and have been jittered a little.
That is, I've added a little bit of random noise so we can see more easily
the density of examples in the feature space.
Here's the scikit-learn code that produced this figure.
We apply grid search here to explore different values of
the optional class weight parameter that controls
how much weight is given to each of the two classes during training.
As it turns out, optimizing for
different evaluation metrics results in
different optimal values of the class weight parameter.
As the class weight parameter increases,
more emphasis will be given to correctly classifying the positive class instances.
The precision-oriented classifier we see here with class weight of two,
tries hard to reduce false negatives while increasing true positives.
So it focuses on the cluster of positive class points in
the lower right corner where there are relatively few negative class points.
Here, precision is over 50 percent.
In contrast, the recall-oriented classifier with class weight of 50,
tries hard to reduce the number of false negatives while increasing true positives.
That is, it tries to find most of
the positive class points as part of its positive class predictions.
We can also see that the decision boundary for
the F1-oriented classifier has an optimal class weight of two,
which is between the optimal class weight values for
the precision and recall-oriented classifiers.
Visually we can see that the F1-oriented classifier also has a kind of
intermediate positioning between the precision and recall-oriented, decision boundaries.
This makes sense given that F1 is the harmonic mean of precision and recall.
The AUC-oriented classifier with
optimal class weight to 5 has a similar decision boundary to the F1-oriented classifier,
but shifted slightly in favor of higher recall.
We can see the precision recall trade-off
very clearly for this classification scenario in
the precision recall curve for
the default support vector classifier with
linear kernel optimized for accuracy on the same dataset,
and using the balanced option for the class weight parameter.
Let's take a look at the code
that generated this plot.
Take a moment to
imagine how the extreme lower right part of
the curve on this precision recall curve represents
a decision boundary that's highly
precision-oriented in the lower right of the classification plot,
where there's a cluster of positive examples.
As the decision threshold is shifted to become less and less conservative,
tracing the curve up into the left,
the classifier becomes more and more like
the recall-oriented support vector classifier example.
Again, the red circle represents
the precision recall trade-off achieved at the zero score mark,
which is the actual decision boundary chosen for the trained classifier.
For simplicity, we've often
used a single train-test split in showing examples of evaluation scoring.
However, using only cross-validation or a test set for
model selection or parameter tuning may still lead
to more subtle forms of overfitting and less
optimistic evaluation estimates for future, unseen data.
An intuitive explanation for this might be the
following: "remember that the whole point of evaluating on
a test set is to estimate how well
a learning algorithm might perform on future, unseen data.
The more information we see about our dataset as part of
repeated cross-validation passes in choosing our model,
the more influence any potential held-up test data
has played into selecting the final model.
Not merely evaluating it.
This is sometimes called data leakage and
we'll describe more about that phenomenon in another module.
So, we haven't done an evaluation with a truly held-out test set unless we
commit to holding back a test split that isn't seen by
any process until the very end of the evaluation.
So that's what's actually done in practice.
There are three data splits: training for model building,
validation for model selection and a test set for the final evaluation.
The training and test sets are typically split out first,
and then cross-validation is run using
the training data to do model and parameter selection.
Again, the test set is not seen until the very end of the evaluation process.
Machine learning researchers take this protocol very seriously.
The train-validate-test design is
a very important universally applied framework for
effective evaluation of machine learning models.
That brings us to the end of this section
of the course on evaluation for machine learning.
You should now understand why accuracy only gives
a partial picture of a classifier's performance and be more familiar with
the motivation and definition of
important alternative evaluation methods and
metrics of machine learning like confusion matrices,
precision, recall, F1 score and area under the RAC curve.
You've also seen how to apply and choose
these different evaluation metric alternatives in order to
optimize model selection or parameter tuning for a classifier,
to maximize a given evaluation metric.
Finally, I'd like to leave you with a couple of points.
First, simple accuracy may not often
be the right goal for your particular machine learning application.
As we saw for example with tumor detection or credit card fraud,
false positives and false negatives might have
very different real-world effects for users or for organization outcomes.
So it's important to select an evaluation metric that
reflects those user application or business needs.
Second, there are a number of other dimensions along which it
may be important to evaluate your machine learning algorithms,
that we don't cover here but that are important for you to be aware of.
I'll mention two specifically here.
Learning curves are used to assess
how a machine learning algorithm's evaluation metric changes or
improves as the algorithm gets more training data.
Learning curves may be useful as part of a cost-benefit analysis.
Gathering training data in the form of
labeled examples is often time-consuming and expensive.
So being able to estimate the likely performance improvement of your classifier,
if you say invest in doubling the amount of training data,
can be a useful analysis.
Second, sensitivity analysis amounts to looking at
how an evaluation metric changes as
small adjustments are made to important model parameters.
This helps assess how robust the model is to choice of parameters.
This may be important to perform especially if there are other costs such as
runtime efficiency that are critical variables when deploying an operational system,
that are correlated with different values of parameter.
For example, decision tree depth or future value threshold.
In this way, a more complete picture of the trade-offs achievable across
different performance dimensions can help you make
the best practical deployment decisions for your machine learning model.
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