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CodeSignal

Classification Algorithms and Metrics

via CodeSignal

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

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