Predictions Techniques in Machine Learning, by Mercè Martín Prats, VP of Applications at BigML. *MLSEV 2020: Virtual Conference.

1. 2nd edition

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Predictions techniques in ML
Mercè Martín
VP of Applications, BigML

3. #MLSEV
So I have a model but...
How do I use it
to predict?

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Operating the Model

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Predicting by default
To produce the most
probable outcome
Using the model

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But what if I...
But accept the
prediction, if over a
probability threshold
Use the model

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Predictions change
vs.
Unbalanced
Over 20%
probability
A LOW THRESHOLD ALSO INCREASES RECALL

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Operating Points

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Choosing the operating point

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Unsupervised

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Clustering by default
To assign the
closest centroid
Using the cluster

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Beware of missings!!!
We cannot tell the
distance
To an empty
numerical field

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Clustering

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Detecting anomalies
To assign the
anomaly score
Using the anomaly
detector

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Removing anomalies
Can improve
generalization of
other models
Removing some
anomalies

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Anomalies

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Discovering associations
To find coocurrences
Using association
discovery

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High lift rules
Are unfrequent but
very strong
relations
Rules with high lift

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Associations

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Predictions examples
CLASSIFICATION
REGRESSION
TIME SERIES FORECASTING
CLUSTER ANALYSIS
ANOMALY DETECTION
ASSOCIATION DISCOVERY
Will this candidate meet the requirements?
How does this employee perform amongst peers?
How many customers will we have tomorrow?
Do these employees share skills?
Is this employee’s behaviour unusual?
Which traits do efficient teams share?

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So are we ready?
ITERATIVE
PREPARING AND
TRANSFORMING DATA
O
PERATIN
G
M
O
D
ELIN
G

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Scenario: call centerr
One call at a time. Will he churn?
•While asking for data, the
operator fills a form that gives
him the prediction.
Single prediction
What’s going to production?

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•Periodically predict a high amount of dataatch predictions)
Using accumulated sales data and exterior
factors (seasonality, holidays, weather
information, etc.) we want to estimate the
daily or weekly sales for each product.
Batch prediction
Scenario: Supermaket sales
What’s going to production?

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Scenario: Industrial chain
•Does the process fail?
•Sensors record current, voltage, speed,
and other physical properties. Predictions
will decide whether the process will have
faults right on the spot.
Local single predictions
What’s going to production?

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Scenario: Online Legal Assistance
•Should we sign a collection of documents?
•Users upload a handful of documents to a
web site to know whether they are correct
and can be signed.
Local batch predictions
What’s going to production?

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Going to production

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Local predictions
https://bigml.com/tools

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A model, not my precioussss

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You need more than a model
Connecting inputs
Connecting outputs
Shift monitoring
Retrain conditions
Training the model
Model integration Model monitoring
Feedback loops