CSE 163

Machine Learning

��Hunter Schafer

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💬Before Class: What is last product you used that uses ML?

🎵Music: Beach Bunny

This Time

• Machine Learning
• Terminology
• Types of ML
• ML Code (scikit-learn)
• Decision Trees

Last Time

• Default Parameters
• Plotting Code
• seaborn
• matplotlib
• Principles of Data Visualization

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Terms

• Machine learning
• Model
• Machine learning algorithm
• Data
• Training Set
• Example
• Feature
• Label
• Types of ML
• Regression
• Classification

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Features Matter

• There exists hundreds of different model types, but the most important thing is having good features to describe your data
• Garbage In => Garbage Out

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Decision Tree

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Code Recap

General ML pipeline (at least to start)

• For classification tasks

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# Separate data

features = data.loc[:, data.columns != 'target']

labels = data['target']

# Create and train model

model = DecisionTreeClassifier()

model.fit(features, labels)

# Predict on some data

predictions = model.predict(features)

# Assess accuracy

accuracy_score(labels, predictions)

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Group Work:

Best Practices

When you first working with this group:

• Introduce yourself!
• If possible, angle one of your screens so that everyone can discuss together

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Tips:

• Starts with making sure everyone agrees to work on the same problem
• Make sure everyone gets a chance to contribute!
• Ask if everyone agrees and periodically ask each other questions!
• Call TAs over for help if you need any!

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Practice Session 1

• Decision Tree Concepts (6 questions)

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TAs will walk around to answer questions! Raise your hand!

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Come back at 11:55!

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Practice Session 2

• DecisionTreeRegressor

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TAs will walk around to answer questions! Raise your hand!

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Come back at 12:15!

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