THIS IS A COMPLETE NOTE FROM ALL BOOKS + LECTURE!
Save your time for internships, other courses by studying over this note!
Are you a 1st/2nd year of Business Analytics Management student at RSM, who want to survive the block 2 Machine Learning module? Are you overwhelmed with 30 pages of re...
4.4.2: Linear Discriminant analysis for p>1
Positive class: the one we care most about predicting
Two types of prediction in prediction
1. Soft prediction : phat = Pr(in pos.class) : expected value between 0 and 1.
a. Soft prediction is more fundamental: hard prediction is done from the
result of soft prediction.
2. Hard prediction: a particular class predicted by comparing the soft prediction phat
to a threshold value
a. hard prediction heavily depends on the decision threshold
Classification metrics
Binary classifier’s error types
1. False positive (e.g., individual who does not defaults to the default)
2. False negative (e.g., individual who defaults to the no default)
Confusion matrix address the interest of displaying the information of which of these
two types of errors are made.
Despite that the overall accuracy might be satisfactory, individual error for FP/FN could
be unacceptably high.
Statistics that reflects the precision and recall :
F1 : harmonic mean of precision and recall
FM : geometric mean of precision and recall.
They both ignore true negatives. : if there is class imbalance, they depend on the positive
cases, so might perform worse in some cases.
Alternatives: correlation coefficients
Correlation between true and predicted class :
Correlation does not explicitly measure prediction accuracy. It only captures
the strength and direction of a linear relationship.
Cohen’s K: 0 is hit when you expect that TN and TP are at the same rate as chance.
, Comparisons of the 4 metrics
Class-specific performance
Sensitivity /recall/TPR : Percentage of true positive that are predicted as positive.
Specificity / True negative rate: Percentage of true negatives that are predicted as
negative.
Precision/Positive predicted value : different from the first two. Proportion of the
positive classified items that are actually positive.
When they are used as pairs…
Precision + recall is useful when the focus is on the positive class
(they ignore TN) ; it does not directly assess predictions for the
negative class.
Recall + specificity treats both positives and negatives as important.
The Bayes classifier works by assigning an observation to the class for which the
posterior probability pk(X) is greatest
- In binary classifier, a value will be assigned to the positive class if
Pr(positive = yes |X=x) >0.5
- This threshold is adjustable.
o Tidymodel uses the default of 0.5 and it is hard to adjust the value
The Bayes classifier will yield the smallest possible total number of misclassified
observations, regardless of the class from which the errors stem.
Thresholds and class imbalance
There is a trade off between the TP and TN, and FP and FN, depends on the threshold
value for the confusion matrix.
By varying the decision threshold, we determine the elements of the confusion matrix.
- The lower the threshold, the more observations classified to the positive class.
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper ArisMaya. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €12,99. Je zit daarna nergens aan vast.