Publications by MSDS 6372: Jacob Turner: Student: Jessica McPhaul link:
JMcPhaul_caseStudy3_spamAssassin_QTW
Case Study 3: Building a Spam Classifier Using Naïve Bayes and Clustering Jessica McPhaul SMU - 7333 - Quantifying the World Date: February 17, 2025 Naïve Bayes Formula Given an email with words \(w_1, w_2, ..., w_n\), the probability that it belongs to spam (\(S\)) is computed as: \[ P(S | w_1, w_2, ..., w_n) = \frac{P(S) \prod_{i=1}^{n} P(w...
103322 sym Python (23380 sym/1 pcs) 2 img 12 tbl
spam dupe?
Predicting Email Spam: A Study Guide with Mathematical and Coding Representation 1. Introduction to Spam Detection Definition Spam detection is a binary classification problem where emails are categorized as either spam (junk) or ham (legitimate). The problem is solved using machine learning techniques, leveraging statistical patterns in emai...
10532 sym Python (1438 sym/2 pcs)
CaseStudy3_SpamAssassin
Case Study 3: Building a Spam Classifier Using Naïve Bayes and Clustering Jessica McPhaul SMU - 7333 - Quantifying the World Date: February 17, 2025 Naïve Bayes Formula Given an email with words \(w_1, w_2, ..., w_n\), the probability that it belongs to spam (\(S\)) is computed as: \[ P(S | w_1, w_2, ..., w_n) = \frac{P(S) \prod_{i=1}^{n} P(w...
56314 sym 2 img 11 tbl
bagging
Bagging: A Study Guide with Mathematical and Coding Representation 1. Introduction to Bagging Definition Bagging (Bootstrap Aggregating) is an ensemble learning technique that improves model stability and accuracy by training multiple models on different random subsets of data and then aggregating their predictions. Bagging is widely used in ...
8321 sym Python (1476 sym/2 pcs)
7333 Module 7 - Decision Trees
Entropy, Gini Coefficient, Partition Trees, Bagging, and Random Forest: A Study Guide 1. Entropy Definition: Entropy is a measure of disorder or randomness in a system. In information theory, entropy quantifies the uncertainty associated with a random variable. It is defined mathematically as: Mathematical Representation: For a discrete random...
20922 sym Python (2597 sym/6 pcs)
Entropy
Entropy: A Study Guide with Mathematical and Coding Representation 1. Introduction to Entropy Definition Entropy is a measure of disorder or uncertainty in a system. In the context of information theory, entropy quantifies the unpredictability of a random variable. The higher the entropy, the more disorderly the system, while lower entropy si...
9211 sym Python (857 sym/2 pcs)
entropy and gini
Entropy and Gini: A Study Guide with Mathematical and Coding Representation 1. Introduction to Entropy and Gini Definition of Entropy Entropy is a measure of disorder or uncertainty in a system. In the context of information theory, entropy quantifies the unpredictability of a random variable. The higher the entropy, the more disorderly the s...
10378 sym Python (1470 sym/3 pcs)
partition trees
Partition Trees: A Study Guide with Mathematical and Coding Representation 1. Introduction to Partition Trees Definition Partition trees, also known as decision trees, are hierarchical models used for classification and regression. They recursively split data into subsets based on feature values to create structured decision paths. Partition ...
6973 sym Python (674 sym/2 pcs)
random forest
Random Forest: A Study Guide with Mathematical and Coding Representation 1. Introduction to Random Forest Definition Random Forest is an ensemble learning technique that extends Bagging (Bootstrap Aggregation) by adding feature randomness in addition to data randomness. It trains multiple decision trees on different bootstrapped samples of th...
8918 sym Python (1069 sym/2 pcs)
spam
Predicting Email Spam: A Study Guide with Mathematical and Coding Representation 1. Introduction to Spam Detection Definition Spam detection is a binary classification problem where emails are categorized as either spam (junk) or ham (legitimate). The problem is solved using machine learning techniques, leveraging statistical patterns in emai...
10532 sym Python (1438 sym/2 pcs)