City College, Fall 2019

Intro to Data Science

Week 8: Performance Evaluation and Ensemble Models

October 28, 2019

Today's Agenda
  1. Model Fit
  2. Anomaly Detection
  3. Ensemble Models
    • Random Forests
    • Gradient Boosting Machines
  4. Midterm Recap
Part I

Evaluating Model Performance

Did we see this coming?

Democratic strategist David Axelrod predicted that poll performance “is going to prompt another round of soul-searching about whether and how you can poll accurately, because a lot of these races that were blowouts tonight or apparently blowouts tonight polled as tough races.”

Democratic pollster Andrew Baumann called the pre-election polls “quite accurate, particularly for a midterm that ended up being totally different than any previous midterm.”

Was this true?
538 Prediction Results
Actual Dem Actual Rep
Predicted Dem 226 2
Predicted Rep 9 197

Based on 538 deluxe model for 11/05/18.

What are the downsides of these measures?
Lift Charts

Rank all observations by the predicted probability class, and chart the cummulative share of actual true values captured by the first x observations, where x ranges from 1 to the total number of observations.

Demonstrates model's ability to outperform other (random) choices at positive prediction across decision thresholds.

For more details.
538 Lift
538 Lift
538 Lift
538 Lift
538 Lift
ROC Curves
ROC = Receiver Operating Characteristic

Plot the true positive rate against the false positive rate at every possible threshold from highest to lowest.

Demonstrates model's ability to outperform other (random) choices across decision thresholds while weighing false positives against false negatives.

For more details.
538 ROC
538 ROC
538 ROC
538 ROC
Can we reduce this to a single number?
AUC = Area Under the Curve

Total volume of area under the ROC curve.

Sci-kit Learn can calculate this for you.

A Digression

538 Prediction Results
Actual Dem Actual Rep Total
Predicted Dem 226 2 228
Predicted Rep 9 197 206
Total 235 199 434

Not everything went according to plan.
Anomaly Detection
The identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
Anomalies in NC-9
Part II

Ensemble Models

Classification Models
  1. Logistic Regression
  2. Decision Trees
  3. Random Forest
  4. Gradient Boosting Machine
  5. Naive Bayes
Review: Survival Rates Among Subgroups
Review: Survival Rates Among Subgroups
Ensemble Models

Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.

Random Forests

Random Forests construct a multitude of decision trees at training time and outputting the class that is the mode of the predicted classes among each tree.

For more details.
And a nice overview of everything.
Random Forests
Gradient Boosting

Gradient Boosting produces a prediction model in the form of an ensemble of weak prediction models and then generalizes them by allowing optimization of an arbitrary loss function.

For more details.
Gradient Boosting
Let's try.

Midterm Exam: November 11, 6:30pm
  • 90 Minute Written Exam
  • No computer needed, closed book, closed notes
  • Part I: Multiple Choice
  • Part II: Short Answer
Week 1: What is Data Science and Why Does It Matter?
  • The data science lifecycle.
  • Famous case studies in data science.
Week 2: Where to Find Data
  • Structured vs unstructured data.
  • Common sources of data.
  • Common ways to access data.
Week 3: Processing and Cleaning Data
  • Elements of the ETL Process
  • Processing Tools
  • Data Cleaning Considerations for Data Scientists
    • Missing Value
    • Handling Outliers
    • Normalizing Data
Week 4: Statistics and the Stories We Tell Ourselves
  • Types of Data
  • Useful Statistical Distribution
  • Important Summary Statistics
  • Independence
  • Key Theorems
Week 5: Intro to Linear Models
  • What Makes Linear Regression Linear
  • Assumptions for Linear Models
  • Measuring Performance for Linear Models
Week 6: Regression vs Classification
  • Regression vs. Classification
  • Logistic Regression
  • Measuring Performance for Classification Models
Week 7: Tree Based Models
  • Linear vs. Nonlinear Models
  • Decision Trees and their Hyperparameters
  • Bias vs.
  • Cross Validation
Week 8: Performance Evaluation and Ensemble Models
  • Lift and ROC Curves
  • Random Forests and Gradient Boosting Machines
  • Cross Validation
Reminder: Assignment 7 Due Monday, November 4 by 6:30pm

DataCamp's Deep Learning with PyTorch

  • The course should appear collectively as assignment within your existing DataCamp account.
  • Each section will appear separately and will be worth oen point toward the total grade for the homework.
  • This will be the last DataCamp assignment of the semester!