Detailed summary of all lectures and additional notes, explanations and examples for the course "Machine Learning" at Tilburg University which is part of the Master Data Science and Society. Course was given by Ç. Güven during the second semester, block four of the academic year 2021 / 2022 (Apri...
Tilburg University
Study Program: Master Data Science and Society
Academic Year 2021/2022, Semester 2, Block 4 (April to June 2022)
Course: Machine Learning (880083-M-6)
Lecturers: Ç. Güven
,Lecture 1: Introduction to Machine Learning
Machine Learning
• Machine Learning means learning from experience
• Concept of Generalization: Algorithm also works with unseen data
Types of learning problems
• Supervised (Classification, Regression) vs Unsupervised Learning (Clustering)
• Multilabel Classification: multiple labels per sample
o Assign songs to one or more genres (for each genre, each song is labeled yes or no)
• Multiclass Classification: one label per sample
o Assign songs to one genre (for each song one label is chosen)
Evaluation
• Mean absolute error: average, absolute difference between true value and predicted value
• Mean squared error: average square of the difference between the true and the predicted
value (more sensitive to outliers, usually larger than MAE)
• Type I error: false positive
• Type II error: false negative
• accuracy compares the true prediction vs the whole set of datapoints
o (TP + TN) / (TP + FN + FP + TN)
• Error rate / misclassification rate
o (FP + FN) / (TP + FN + FP + TN)
• Accuracy and error rate are only useful if the dataset is balanced
• precision is the hit-rate (true positives vs the ones predicted as positives)
o “What fraction of flagged emails are real SPAM?”
o (TP) / (TP + FP)
• recall is the true positive rate (true positives vs the actual positives)
o “What fraction of real SPAM has been flagged?”
o (TP) / (TP + FN)
• F or F1 score combines precision and recall and comes up with a harmonic mean of the two
o 2* [ ( (TP) / (TP + FP) ) * ( (TP) / (TP + FN) ) ] / [ ( (TP) / (TP + FP) ) + ( (TP) / (TP + FN) ) ]
o 2* [ Precision * Recall ] / [ Precision + Recall ]
• Use F beta to give more weight to recall or precision
o > 1: recall is weighted more
o < 1, precision is weighted more
, • When there are more than two classes use micro and macro average
o Macro average
▪ rare classes have the same impact as frequent classes (don’t use this one
when the classes are not balanced!)
▪ Compute precision and recall per-class, and average them
o Micro average
▪ Micro averaging treats the entire set of data as an aggregate result, and
calculates 1 metric rather than k metrics that get averaged together
▪
o Macro F1-Score is the harmonic mean of Macro-Precision and Macro-Recall
Find the best possible solution
• We are trying to approximate the relation between the input and the target value
• For a single value, the loss function captures the difference between the predicted and the
true target value
• Cost Function is the loss function plus a regularization term
→ find the parameters which minimize the cost function
• Empirical risk minimization: we are trying to minimize the risk on the sample set
o If the risk is represented by MAE:
o calculate average difference between
estimated cost function and the true cost
function → minimize that one
• ̂
𝑓 (𝑥) can be a linear function or more complex (polynomial function). The higher the power,
the more complex the model.
o If 𝑓̂(𝑥) = 𝜃𝑥 + 𝑐 (linear):
o Use training and validation data to find hyperparameter theta and power
• Optimal solution minimizes the loss between 𝑓(𝑥) and 𝑓̂(𝑥)
• Use a polynomial function for more complex relationships
• A higher power p implies higher degree of freedom = flexibility
• Use cross validation to find the best hyperparameter p
Regularization
•
• Add lambda as regularization term to the cost function to regulate theta to avoid overfitting
o Large value of lambda reduces the size of theta term and overfitting since a simpler
model is assumed
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