Class Introduction

• Instructor: Ferdi Eruysal

• Attendance: An attendance sheet is circulating; students must sign before leaving.

• Assignment: An assignment was posted yesterday.

• Questions: Instructor asks students about their understanding of the assignment.

Assignment Details

• Task Objective: Build two models - 1) Decision Tree (D3) and 2) Logistic Regression (LogReg).

• Dataset: Telecom data focused on churn.

• Hyperparameters Optimization:

  • Students need to optimize specific hyperparameters mentioned by the instructor.

  • Determine which metric to optimize for: accuracy, precision, recall, or F1 Score.

• Comparison:

  • Evaluate performance of Decision Tree against Logistic Regression.

  • Students should work on the assignment for about 2 hours.

• Feature Engineering:

  • Students encouraged to create new columns from existing ones for potential bonus points.

  • Basic assignments use existing columns; engineering may lead to improved model scores.

• Report Requirements: Students must submit a brief report detailing their processes, findings, and include screenshots if necessary.

Logistic Regression Overview

• Classification Problems: Logistic regression is used to tackle classification problems, as opposed to linear regression which is used for regression-type problems.

• Mechanism:

  • The approach involves transitioning from a linear regression line to an S-shaped logistic curve to enhance prediction accuracy (0 or 1 outcomes).

  • This transition disrupts the linear assumptions which require a new loss function.

• Loss Function:

  • Total error calculated in machine learning; important for measuring performance.

Overfitting and Regularization

• Overfitting:

  • Occurs when the model is too complex and captures noise rather than the intended signal.

• Regularization:

  • A technique used to prevent overfitting, through methods like pruning (in decision trees) and imposing penalties on model complexity in logistic regression.

  • Key Parameters:

  • Lambda (λ) and Alpha (α) control model complexity.

  • Alpha must always be between 0 and 1.

  • Lambda can be any positive number.

K-Nearest Neighbors (KNN) Algorithm

• Introduction: Introduction to KNN as a simple yet powerful machine learning algorithm.

• Concept:

  • The algorithm predicts a class based on the closest points.

  • K Value: The number of neighboring data points considered for prediction. Odd values for K are preferred to avoid ties in votes for class prediction.

• Distance Metric:

  • Euclidean Distance Calculation: The methodology to measure the distance between data points:
    d = ext{sqrt}((a1 - b1)^2 + (a2 - b2)^2 + ext{…} + (an - bn)^2)

• Data Types: KNN automatically handles one-hot encoding for categorical variables but may struggle with many categorical variables.

Choosing the Optimal K Value

• Small K Value Risks:

  • May lead to overfitting since it closely follows the training data points.

• Large K Value Issues:

  • Results in a simplistic model that averages predictions across many neighbors, potentially missing nuanced insights.

• Best K Value: Aim to find the K value that yields the highest performance across accuracy, precision, or recall depending on the problem context.

Practical Example and Exercise Guide

• Hands-on Exercise:

  • Assignments involve building decision trees, logistic regression, and KNN models.

  • Each student must determine the best hyperparameters based on optimization goals set by the instructor.

  • Focus on recall for churn prediction.

• Weekly Materials:

  • Available for downloading datasets and models.

• Common Errors and Debugging:

  • Address typical errors (e.g., Java-related program crashes) encountered during model building and parameter optimization.

• Performance Assessment: Regularly check performance metrics (accuracy, precision, recall) to guide model refinement and ensure alignment with business needs.

Conclusion and Wrap-up

• Each student is tasked to find optimal hyperparameters for their individual assignments before the next class.

• Instructor emphasized the importance of hands-on practice to reinforce understanding and mastery of machine learning concepts.

• Questions from students encouraged throughout the learning process, with direct support provided by the instructor.