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Machine Learning Demystified Michelle Hardwick Director, Data Science & Analytics Salt Lake Community College
Transcript of Machine Learning Demystified - Store & Retrieve … › utoug.documents › ...Supervised Machine...
Machine Learning Demystified
Michelle HardwickDirector, Data Science & Analytics
Salt Lake Community College
Director, Data Science & Analytics at Salt Lake Community College
Adjunct Professor at Utah State University
Oracle ACE Director, IOUG Executive Vice President, UTOUG President
About Me
Twitter: @datacheesehead
Key Concepts
What is Machine Learning?
Algorithms
Agenda
Pop Quiz
Machine Learning Process
Applications of Machine Learning
What is Machine Learning?
Machine Learning
is the Process of Finding
Insightful Patterns
in your Data
Why is machine learning important?
Presenter
Presentation Notes
Volume of data is growing and will continue to do so Data is being generated not only from people but from devices
How is machine learning being used?
Google search
Recommendation systems
Fraud detection
Tagging people in photos
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It’s all around us but it might not be readily apparent Example in google search: what to show you? If you are a developer and you search java, are you shown java programming docs or coffee sites?
Algorithms
Anomaly Detection
Regression
Classification
Clustering
Association
Support Vector Machines
Decision Tree Learning
Instance-Based Learning
Generalized Linear Models
Centroid-Based Clustering
Hierarchical Clustering
Density-Based Clustering
Problem Type Algorithm Family AlgorithmOne-Class SVM
Linear SVM
Non-Linear SVM
Classification/Regression Decision Tree
Random Forest
Isolation Forest
Radius Neighbors
K-Nearest Neighbors
Logistic Regression
Bayesian Naïve Classifier
Linear Regression
Bayesian Linear Regression
Feedforward ANN (Multilayer Perceptron)
K-Means Clustering
Complete-Linkage Clustering
Single-Linkage Clustering
Average-Linkage Clustering
DBSCAN
Association Rules
Artificial Neural Network
Unsupervised Machine
Learning
When we do not know what the output
values should be
Slide source: Toward Data Science blog
Used when we wish to learn the inherent structure of our data
without using explicitly-provided labels
Supervised Machine
Learning
When we have prior knowledge to know
what the output values should be
Dimension Reductionality
Learn about the data to find the dimensions that interrelate the features. Used for
eliminating redundant features to speed up data processing.
Types:
Regression
Supervised
Classification
Identifying to which category an object belongs
Types:
Decision Tree
Support Vector Machine
Logistic Regression
Neural Networks
Instance Based Learning
Supervised
Regression
Predicting a continuous valued attribute associated with an object. Regression predicts how
much something will happen.
Types:
Generalized Linear Model
Support Vector Machine
Neural Networks
Decision Tree
Supervised
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Presentation Notes
Regression is similar to classification but classification predicts whether something will happen. Regression predicts how much that something will happen.
Naïve Bayes
Finds the probability of an event occurring given the probability of another event that has
already occurred
Types:
Bayes Theorem
Supervised
Exploratory Analysis
Used to automatically identify structure in the data
Types:
Clustering
Unsupervised
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Clustering is dividing the data into smaller related subsets, or clusters
Association
Discover the rules that describe large portions of the data
Such as: People who buy X also tend to buy Y
Types:
Association Rules
Decision Tree
Unsupervised
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Sometimes referred to as market basket analysis
Anomaly Detection
Finds cases that are unusual or slightly different
Types:
Support Vector Machine
Decision Tree
Supervised or Unsupervised
Presenter
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Sometimes referred to as market basket analysis
How do you
pick which
algorithm to run?
ActivityWhat algorithm families would you run for the following ML problem?
What students are most likely to succeed at SLCC?
Anomaly Detection
Regression
Classification
Clustering
Association
Support Vector Machines
Decision Tree Learning
Instance-Based Learning
Generalized Linear Models
Centroid-Based Clustering
Hierarchical Clustering
Density-Based Clustering
Problem Type Algorithm Family AlgorithmOne-Class SVM
Linear SVM
Non-Linear SVM
Classification/Regression Decision Tree
Random Forest
Isolation Forest
Radius Neighbors
K-Nearest Neighbors
Logistic Regression
Bayesian Naïve Classifier
Linear Regression
Bayesian Linear Regression
Feedforward ANN (Multilayer Perceptron)
K-Means Clustering
Complete-Linkage Clustering
Single-Linkage Clustering
Average-Linkage Clustering
DBSCAN
Association Rules
Artificial Neural Network
Key ML Concepts
Train vs Test
For Supervised Learning, we want to split out a portion of our dataset to do testing on to
validate the accuracy of our predictions
This should be done randomly
Typical splits are 70% Train and 30% Test or 80%/20%
Test dataset will have the output field but you will ignore it when running the model
Presenter
Presentation Notes
Another method I’ve heard of is train on data up to last quarter then test on last quarter. If your business is cyclical, this is not a good idea as you may be introducing bias.
Feature Engineering
Feature engineering is the process of transforming the raw data into features that will
better represent the underlying problem, resulting in improved model accuracy
The Attribute Importance model can be used for this (minimum description length
algorithm)
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Examples: Flight delay data. If the field is date & time, remove date to look at the hour of the day that flights are delayed. Student ages. We know that traditional students behave differently than non-traditional students. We can categorize them instead of using age as a raw number. City & State. We know that city is not useful without the state. Make these one field.
Bias
When an algorithm produces results that are systematically prejudiced due to erroneous
assumptions in the ML process
This is usually related to the gathering or usage of data
You should check your data, models and results for this bias
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You’ve probably heard about stereotyping & confirmation bias
The Machine Learning Process
Training
Step 03
Testing
Step 04
Data Preparation
Step 02
Evaluation
Step 05
Gather Data
Step 01
The ML Process
Step 1: Gather Data
To improve the accuracy of the predictions,
Quantity and Quality of the data is most important
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The size of your data and the quality of it can make or break your ML project. Most errors in results can be traced back to errors in the data.
Step 1: Gather DataProfile data for quality
Reliable data avoids:
• Duplicated data
• Bad labels
• Bad values
• Omitted values
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Duplicated data: Same log loaded twice Bad labels: Someone mislabeled a picture of an oak tree as a maple Bad values: Extra digit, decimal point missing Omitted values: Someone forgot to enter a value
Step 1: Gather DataIdentify Features
A feature is a measurable property of the object you are trying to analyze.
These are data points that describe the object. Such as age, gender, zip code, etc.
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Features are sometimes referred to as variables or attributes
Step 1: Gather DataLabel Sources
If your training data is not classified with the outcome, it needs to be labeled
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Label is the term used for the final output definition
Step 2: Data Preparation
Perhaps the most time consuming task is preparing your data for the algorithm you’ll be
running
Where should you transform? Prior to the training? Or in the model?
Presenter
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Transforming prior to training In this approach, we perform the transformation before training. This code lives separate from your machine learning model. Pros Computation is performed only once. Computation can look at entire dataset to determine the transformation. Cons Transformations need to be reproduced at prediction time. Beware of skew! Any transformation changes require rerunning data generation, leading to slower iterations. Skew is more dangerous for cases involving online serving. In offline serving, you might be able to reuse the code that generates your training data. In online serving, the code that creates your dataset and the code used to handle live traffic are almost necessarily different, which makes it easy to introduce skew. Transforming within the model For this approach, the transformation is part of the model code. The model takes in untransformed data as input and will transform it within the model. Pros Easy iterations. If you change the transformations, you can still use the same data files. You're guaranteed the same transformations at training and prediction time. Cons Expensive transforms can increase model latency. Transformations are per batch. There are many considerations for transforming per batch. Suppose you want to normalize a feature by its average value--that is, you want to change the feature values to have mean 0 and standard deviation 1. When transforming inside the model, this normalization will have access to only one batch of data, not the full dataset. You can either normalize by the average value within a batch (dangerous if batches are highly variant), or precompute the average and fix it as a constant in the model. We'll explore normalization in the next section. �
Step 2: Data Preparation
Numeric Transformations
Convert non-numeric data to numeric
Step 2: Data Preparation
Numeric Normalization
Transform features to be on the same scale
Methods:
• Scaling to a range
• Clipping
• Log scaling
• Z-score
Step 2: Data Preparation
Bucketing
For numeric data where there is not a linear relationship, you can bucket it
Two Types of Bucketing:
• Equal spaced boundaries
• Quantile boundaries
Images source: Google machine learning
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Quantile boundaries have the same number of records in each bucket
Step 2: Data Preparation
Transforming Categorial Data
When feature data is not an ordered relationship
Two methods:
• Vocabulary
• Hashing
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Vocabulary creates a unique feature for each category value Hashing is better for datasets that change but can cause collisions
Step 3: Training
At this point you’ll put together your machine learning model
You can use many tools for this
Build the model, accepting defaults, then run it
Presenter
Presentation Notes
My fav tools that I’ve used are R and Oracle Data Miner
Step 4: Testing
Now run your model (with the same parameter settings) against your test dataset
Step 5: Evaluation
Check the accuracy of your model
How many of your test records did the model predict correctly?
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What percent of accuracy is good?
Repeat and Repeat and Repeat
Applications of Machine Learning
Demo Time!
