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Classification Algorithms and Metrics

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Overview

Go beneath the surface of classification algorithms and metrics, implementing them from scratch for deeper understanding. Bypass commonly-used libraries such as scikit-learn to construct Logistic Regression, k-Nearest Neighbors, Naive Bayes Classifier, and Decision Trees from ground up. This course includes creating the AUCROC metric for Logistic Regression, among others.

Syllabus

Unit 1: Understanding the Confusion Matrix, Precision, and Recall in Classification Metrics

Calculating True Negatives and False Positives in Medical Diagnostics
Precision and Recall in Medical Diagnostics
Precision Calculation in Medical Diagnostics
Medical Test Recall Calculation Correction
Calculating Precision and Recall in Medical Diagnostics

Unit 2: Implementing and Interpreting AUCROC for Logistic Regression Models

Evaluating the Diagnostic Test with AUC-ROC
Fine-Tuning Thresholds for ROC Curve Analysis
Diagnostic Test AUCROC Calculation Correction
Calculating the AUC-ROC Metric
Plotting the ROC curve

Unit 3: Implementing k-Nearest Neighbors Algorithm in C++

Adjusting k in k-Nearest Neighbors Classifier
Manhattan Distance in k-NN Classifier
Fruit Ripeness Classification Conundrum
Classifying Fruit Ripeness with k-NN
Navigating the Cosmos: Implementing k-NN Majority Vote

Unit 4: Implementing the Naive Bayes Classifier from Scratch in C++

Predicting the Weather with Naive Bayes
Forecast Predictor: Calculating Prior Probabilities
Calculating Normalized Values in Weather Data Analysis
Predict the Play Day with Naive Bayes

Unit 5: Understanding and Implementing Decision Tree Splits

Exploring Gini Index in Movies Dataset
Calculating the Weighted Gini Index
Debugging Gini Index Calculation in Movie Recommendations
Implementing the Dataset Split Function
Calculating the Gini Index for a Movie Recommendation System

Unit 6: Building a Decision Tree from Scratch in C++

Building and Visualizing a Decision Tree from Scratch
Creating Terminal Nodes for Decision Trees
Recursive Tree Splitting Challenge

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