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.
Clustering
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Applied Machine Learning in Python
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Module 4: Supervised Machine Learning - Part 2
This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
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Kevyn Collins-ThompsonAssociate Professor
School of Information
Aside from transformations,
the other family of unsupervised learning methods are the clustering methods.
The goal of clustering is to find a way to divide up a data set into groups called
clusters.
So that groups with similar data instances are assigned to the same cluster,
while very dissimilar objects are assigned to different clusters.
If new data points were being added over time, some clustering algorithms could
also predict which cluster a new data instance should be assigned to.
Similar to classification, but
without being able to train the clustering model using label examples in advanced.
One of the most widely used clustering algorithms is called k-means clustering.
K-means clustering finds k cluster centers in different
regions of the feature space that it thinks represent very different groups.
You need to specify the value of k ahead of time,
which is one of the draw backs of k-means.
For some problems, we may know the number of classes the data should fall into, but
for many other tasks, we might not.
K-means operates by first randomly picking locations for the k-cluster centers.
Then it goes back and forth between two steps.
In the first step, given the locations of existing cluster centers, it assigns
each data point to a cluster center based on its distance from the center.
In other words, it assigns each data point to the closest center.
Then in the second step, it adjusts the locations of each cluster center.
It does this by setting the new cluster center
to the mean of the positions of all the data points in that cluster.
Somewhat magically, after running this alternating process for
a while, coherent clusters do start to form.
And the cluster centers and the corresponding cluster assignment for
each data point eventually settled down to something stable.
Now one aspect of k means is that different random starting points for
the cluster centers often result in very different clustering solutions.
So typically, the k-means algorithm is run in scikit-learn with ten different
random initializations.
And the solution occurring the most number of times is chosen.
Here is a step by step example.
We first choose three locations in the space randomly to be the cluster centers.
Then we assign each data point to the cluster with the nearest center.
Now for each cluster, we compute the mean location of all points in the cluster and
use that as the new cluster center for the next iteration.
Here's the second iteration of the first and second steps.
Eventually, after 20 or 50 or 100 steps,
things settle down to converge on one solution, as shown here.
K-means clustering is simple to apply in scikit learning.
You import the k-means class from sklearn cluster create the k-means object
set into value of k by specifying the n cluster parameter, and
then calling the fit method on the dataset to run the algorithm.
One distinction should be made here between clustering algorithms that can
predict which center new data points should be assigned to, and
those that cannot make such predictions.
K-means supports the predict method, and so we can call the fit and
predict methods separately.
Later methods we'll look at like agglomerative clustering do not and
must perform the fit and predict in a single step, as we'll see.
Here's the output from the notebook code showing the result supplied
to the fruits dataset, where we know the value of k ahead of time.
Note that kmeans is very sensitive to the range of future values.
So if your data has features with very different ranges, it's important to
normalize using min-max scaling, as we did for some supervised learning methods.
Because each cluster in k-means clustering is defined entirely by its center point,
it can only capture fairly simple types of clusters.
K-means clustering tends to work well when the data points form into groups
of roughly the same size, with simple globular shapes that are well-separated.
K-means will tend not to do well if the data forms long,
irregular clusters, for example.
Also the version k-means we saw here assumed that the data features
were continuous values.
However, in some cases we may have categorical features,
where taking the mean doesn't make sense.
In that case,
there are variants of k-means that can use a more general definition of distance.
Such as the k-medoids algorithm that can work with categorical features.
A glommer of clustering refers to a family of clustering methods that work
by doing an iterative bottom up approach.
First, each data point is put into its own cluster of one item.
Then, a sequence of clusterings are done where the most similar
two clusters at each stage are merged into a new cluster.
Then, this process is repeated until some stopping condition is met.
In scikit-learn, the stopping condition is the number of clusters.
Here's a visual example of how agglomerative clustering might proceed on
a sample dataset until three clusters are reached.
In Stage 1, each data point is in its own cluster,
shown by the circles around the points.
In Stage 2, the two most similar clusters,
which at this stage amounts to defining the closest points are merged.
And this process is continued, as denoted by the expanding and
closed regions that denote each cluster.
You can choose how the agglomerative clustering algorithm determines the most
similar cluster by specifying one of several possible linkage criteria.
In scikit-learn, the following three linkage criteria are available, ward,
average, and complete.
Ward's method chooses to merge the two clusters that give the smallest increase
in total variance within all clusters.
Average linkage merges the two clusters that have the smallest average distance
between points.
Complete linkage, which is also known as maximum linkage, merges the two clusters
that have the smallest maximum distance between their points.
In general, Ward's method works well on most data sets, and
that's our usual method of choice.
In some cases, if you expect the sizes of the clusters to be very different, for
example, that one cluster is much larger than the rest.
It's worth trying average and complete linkage criteria as well.
To perform agglomerative clustering in scikit-learn,
you import the agglomerative clustering class from sklearn cluster.
When initializing the object, you specify the n clusters parameter
that causes the algorithm to stop when it has reach that number of clusters.
You call the fit predict method using the data set as input and
they return the set of cluster assignments for the data points as shown here.
One of the nice things about agglomerative clustering is that it
automatically arranges the data into a hierarchy as an effect of the algorithm,
reflecting the order and
cluster distance at which each data point is assigned to successive clusters.
This hierarchy can be useful to visualize using what's called a dendrogram,
which can be used even with higher dimensional data.
Here's the dendogram corresponding to the Ward's method clustering of the previous
data set example.
The data points are at the bottom and are numbered.
The y axis represents cluster distance, namely,
the distance that two clusters are apart in the data space.
The data points form the leaves of the tree at the bottom, and the new node
parent in the tree is added as each pair of successive clusters is merged.
The height of the node parent along the y axis
captures how far apart the two clusters were when they merged,
with the branch going up representing the new merged cluster.
Note that you can tell how far apart the merged clusters are by the length of each
branch of the tree.
This property of a dendogram can help us figure out the right number of clusters.
In general, we want clusters that have highly similar items within each cluster,
but that are far apart from other clusters.
For example,
we can see that going from three clusters to two happens at a fairly high Y value.
Which means the clusters that were merged were a significant distance apart.
We might want to avoid choosing two clusters and
stick with three clusters that don't involve forcing a merge for
clusters that have very dissimilar items in them.
Scikit-learn doesn't provide the ability to plot dendrograms, but SciPy does.
SciPy handles clustering a little differently than scikit-learn, but
here is an example.
We first import the dendrogram in word functions from the scipy.cluster
hierarchy module.
The word function returns an array that specifies the distances spanned during
the agglomerative clustering.
This word function returns a linkage array,
which can then be passed to the dendogram function to plot the tree.
Typically, making use of this hierarchy is most useful when the underlying data
itself follows some kind of hierarchical process so the tree is easily interpreted.
For example, hierarchical clustering is especially useful for genetic and
other biological data where the levels represent stages of mutation or evolution.
But there are other data sets where both k-means clustering and
agglomerative clustering don't perform well.
So we're now going to give an overview of a third clustering method called DBSCAN.
DBSCAN is an acronym that stands for
density-based spatial clustering of applications with noise.
One advantage of DBSCAN is that you don't need to specify the number of clusters in
advance.
Another advantage is that it works well with datasets that have more complex
cluster shapes.
It can also find points that are outliers that shouldn't reasonably be assigned to
any cluster.
DBSCAN is relatively efficient and can be used for large datasets.
The main idea behind DBSCAN is that clusters represent areas
in the dataspace that are more dense with data points, while being separated by
regions that are empty or at least much less densely populated.
The two main parameters for DBSCAN are min samples and eps.
All points that lie in a more dense region are called core samples.
For a given data point, if there are min sample of other data points that
lie within a distance of eps, that given data points is labeled as a core sample.
Then, all core samples that are with a distance of eps units apart
are put into the same cluster.
In addition to points being categorized as core samples,
points that don't end up belonging to any cluster are considered as noise.
While points that are within a distance of eps units from core points, but
not core points themselves, are termed boundary points.
Here's an example of DBSCAN applied to a sample data set.
As with the other clustering methods,
DBSCAN is imported from the Scikit-Learn cluster module.
And just like with a agglomerative clustering,
DBSCAN doesn't make cluster assignments from new data.
So we use the fit predict method to cluster and
get the cluster assignments back in one step.
One consequence of not having the right settings of eps and min samples for
your particular dataset might be that the cluster memberships returned
by DBSCAN may all be assigned the label -1, which indicates noise.
Basically, the EPS setting does implicitly
control the number of clusters that are found.
With DBSCAN, if you've scaled your data using a standard scalar or
min-max scalar to make sure the feature values have comparable ranges,
finding an appropriate value for eps is a bit easer to do.
One final note, make sure that when you use the cluster assignments from DBSCAN,
you check for and handle the -1 noise value appropriately.
Since this negative value might cause problems, for
example, if the cluster assignment is used as an index into another array later on.
Unlike supervised learning, where we have existing labels or
target values to use for evaluating the effectiveness of the learning method, it
can be difficult to evaluate unsupervised learning algorithms automatically.
Since there's typically no ground truth to compare against.
In some cases, as in the breast cancer example, we may have existing labels that
can be used to evaluate the quality of the clusters by comparing the assignment of
a data point to a cluster with the label assigned to the same data point.
But there are many cases where labels are not available.
In addition, in the case of clustering, for example, there's ambiguity,
in a sense that there are typically multiple clusterings that could be
plausibly assigned to a given data set.
And none of them is obviously better than another unless we have some additional
criteria.
Such as, performance on the specific application task
that does have an objective evaluation to use as a basis for comparison.
For example, in cases where the results of the clustering are used as features for
supervised learning, we could use the overall classifier accuracy gain from
adding these clustering-based features as a measure of success for
the underlying clustering.
Another issue with evaluating clustering algorithms is that it can be hard to
automatically interpret or label the meaning of the clusters that are found.
And this is still a step that requires human expertise to judge.
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