Study Notes from Class with Ferdi Eruysal

Introduction

• Instructor: Ferdi Eruysal

• Class Routine: Starting discussions and checking attendance

• Overview: Today's session focuses on elevating model performance and metrics for classification and regression.

Attendance

• Attendance sheet is circulated for student initials.

Class Focus

• Review of previous lessons on basic metrics for evaluating model performance.

• In-depth discussion on enhancing model performance.

• Introduction of new metrics relevant to classification and regression problems.

• The instructor plans to clarify the assignment problem towards the conclusion of the session.

Class Atmosphere

• The instructor notes a general feeling of unhappiness among students, speculating it may be due to weather or impending examinations.

• Comments on weather conditions and personal anecdotes related to experiencing snow.

Key Concepts Introduced

Bias in Model Predictions

• Definition of Bias: In machine learning, bias refers to a model's tendency to consistently make incorrect predictions.

  • Example: Aiming at a bull's eye but always hitting the top left or right corner far from the target.

• Implications of high bias: Models may display positive or negative types of errors in prediction.

Variance in Model Predictions

• Definition of Variance: Variance indicates how much a model's predictions vary for different datasets.

  • Key Insight: A model with high variance is significantly influenced by the choice of the training dataset.

  • Visual Explanation: Hitting close to the bull's eye consistently (low variance) versus wildly varying predictions (high variance).

Trade-off between Bias and Variance

• Fundamental Trade-off: There is a trade-off between bias and variance. If one is minimized, the other typically increases.

  • Scenario: Simplifying a model (high bias) might reduce the complexity of data representations but increase variance.

  • Aim: To find a machine learning model that maintains low bias and low variance, which is ideal for most contexts.

Model Examples Explained

• High-Bias, Low-Variance Model: Predictions cluster closely but are far from the actual value.

• High-Bias, High-Variance Model: Predictions are scattered significantly, displaying both incorrect clustering and inconsistency.

• Low-Bias, Low-Variance Model: A desired model type where predictions hover around actual values with low variability.

Model Evaluation Techniques

Error Analysis

• Methods of identifying bias include analyzing error terms.

• High bias can lead to systematic over or under-predictions. Example: Height predictions that are consistently 2-3 inches off.

Histographic Analysis

• Usage of histograms to visualize error distributions aids in distinguishing bias and variance issues.

• Normal distributions indicate lower variability, while significant deviations signal high variance.

Practical Applications

Assignment Preparation

• Emphasizes upcoming sessions to analyze error terms using RapidMiner.

• Analysis of bias and variance trade-offs will be integrated with team projects.

Introductory Metrics for Regression Problems

• R-Squared Value: Proportion of variance in the dependent variable that is predictable from the independent variables, e.g., ( R^2 = 0.72 ) indicates 72% variance explained.

• Root Mean Square Error (RMSE): Calculated as follows:

  • [ RMSE = \sqrt{\frac{1}{n} \sum{i=1}^{n} (yi - \hat{y}_i)^2} ]

  • Important to note: RMSE can be disproportionately impacted by extreme errors due to squaring residuals.

Alternative Error Metrics

• Mean Absolute Error (MAE):

  • Measures the average absolute differences between predicted and actual values:

  • [ MAE = \frac{1}{n} \sum{i=1}^{n} |yi - \hat{y}_i| ]

  • Use case: Better for analyzing real-world scenarios without outlier bias.

• Mean Absolute Percentage Error (MAPE): Expresses error as a percentage.

Introduction to Classification Metrics

Definition of Key Terms

• Gain: Measures the effective increase in accuracy of targeted communications (based on model predictions) based on a selected subset.

• Lift: Calculation of the likelihood of an event occurring given a selection model compared to a random selection.

Example for Classification Metrics

• Market scenario example contrasting general purchasing probabilities versus model-winning probabilities to assess effectiveness in reaching likely customers.

Data Utilization in Models

• Students are instructed to access datasets in Canvas to perform predictive modeling exercises, with an emphasis on target variables.

Application of Models in AI Studio

Practical Session

• Ongoing practical session on building a decision tree and evaluating its performance. Students work with provided datasets (e.g. customer_training.csv).

• Criteria discussed: Optimize model parameters while ensuring clarity and functionality.

Participants Engagement and Questions

• Interactive responses and behavioral observations from students throughout the session are highlighted.

• Student inquiries shape the responses and clarifications delivered by the instructor, fostering a cooperative learning environment.