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Introduction to Machine Learning Classification: Tasks Sonar Learning goals 0.20 Understand the main difference between regression and 0.15 classification Response V2 M R 0.10 Know that classification can be binary or multiclass 0.05

  1. Introduction to Machine Learning Classification: Tasks Sonar Learning goals 0.20 Understand the main difference between regression and 0.15 classification Response V2 M R 0.10 Know that classification can be binary or multiclass 0.05 Know some examples of classification tasks 0.00 0.00 0.01 0.02 0.03 0.04 0.05 V1

  2. CLASSIFICATION Learn functions that assign class labels to observation / feature vectors. Each observation belongs to exactly one class. The main difference to regression is the scale of the output / label. Our Data Sepal Sepal Petal Petal Species Length Width Length Width 5.1 3.5 1.4 0.2 setosa 5.9 3.0 5.1 1.8 virginica Classifier New Data with New Class label unknown label Sepal Sepal Petal Petal Species Length Width Length Width 5.4 3.3 3.2 1.1 ??? � c Introduction to Machine Learning – 1 / 7

  3. BINARY AND MULTICLASS TASKS The task can contain 2 classes (binary) or multiple (multiclass). Sonar Iris 2.5 0.20 2.0 0.15 1.5 Response Petal.Width Response setosa V2 M versicolor R 0.10 virginica 1.0 0.05 0.5 0.00 0.0 0.00 0.01 0.02 0.03 0.04 0.05 2 4 6 V1 Petal.Length � c Introduction to Machine Learning – 2 / 7

  4. BINARY CLASSIFICATION TASK - EXAMPLES Credit risk prediction, based on personal data and transactions Spam detection, based on textual features Churn prediction, based on customer behavior Predisposition for specific illness, based on genetic data https://www.bendbulletin.com/localstate/deschutescounty/3430324-151/fact-or-fiction-polygraphs-just-an-investigative-tool � c Introduction to Machine Learning – 3 / 7

  5. MULTICLASS TASK - MEDICAL DIAGNOSIS https://symptoms.webmd.com � c Introduction to Machine Learning – 4 / 7

  6. MULTICLASS TASK - IRIS The iris dataset was introduced by the statistician Ronald Fisher and is one of the most frequent used data sets. Originally, it was designed for linear discriminant analysis. Setosa Versicolor Virginica Source: https://en.wikipedia.org/wiki/Iris_flower_data_set � c Introduction to Machine Learning – 5 / 7

  7. MULTICLASS TASK - IRIS 150 iris flowers Predict subspecies Based on sepal and petal length / width in [cm] ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1: 5.1 3.5 1.4 0.2 setosa ## 2: 4.9 3.0 1.4 0.2 setosa ## 3: 4.7 3.2 1.3 0.2 setosa ## 4: 4.6 3.1 1.5 0.2 setosa ## 5: 5.0 3.6 1.4 0.2 setosa ## --- ## 146: 6.7 3.0 5.2 2.3 virginica ## 147: 6.3 2.5 5.0 1.9 virginica ## 148: 6.5 3.0 5.2 2.0 virginica ## 149: 6.2 3.4 5.4 2.3 virginica ## 150: 5.9 3.0 5.1 1.8 virginica � c Introduction to Machine Learning – 6 / 7

  8. MULTICLASS TASK - IRIS Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1.2 Sepal.Length Corr: −0.118 Corr: 0.872*** Corr: 0.818*** setosa: 0.743*** setosa: 0.267. setosa: 0.278. 0.8 versicolor: 0.526*** versicolor: 0.754*** versicolor: 0.546*** 0.4 virginica: 0.457*** virginica: 0.864*** virginica: 0.281* 0.0 4.5 Corr: −0.428*** Corr: −0.366*** Sepal.Width 4.0 setosa: 0.178 setosa: 0.233 3.5 3.0 versicolor: 0.561*** versicolor: 0.664*** 2.5 virginica: 0.401** virginica: 0.538*** 2.0 Corr: 0.963*** Petal.Length 6 setosa: 0.332* 4 versicolor: 0.787*** virginica: 0.322* 2 2.5 Petal.Width 2.0 1.5 1.0 0.5 0.0 7.5 5.0 2.5 Species 0.0 7.5 5.0 2.5 0.0 7.5 5.0 2.5 0.0 5 6 7 8 2.0 2.5 3.0 3.5 4.0 4.5 2 4 6 0.0 0.5 1.0 1.5 2.0 2.5 setosaversicolor virginica � c Introduction to Machine Learning – 7 / 7

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