Aop 1.Kol

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| By Annwyn
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Annwyn
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Quizzes Created: 7 | Total Attempts: 7,178
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  • 1/63 Questions

    Vizualizacija podataka je:

    • Veoma znacajna za nauku o podacima
    • Znacajna je samo za dvodimenzionalne podatke
    • Nije relevantna za nauku o podacima
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Aop 1.Kol - Quiz
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  • 2. 

    Sekvencijalni podaci se razlikuju po tome sto:

    • Redosled nije bitan

    • Ne manjeju znacenje ako se permutuju

    • Redosled je bitan

    Correct Answer
    A. Redosled je bitan
    Explanation
    The correct answer is "Redosled je bitan." This means that the order of sequential data is important and changing the order can alter the meaning or significance of the data.

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  • 3. 

    Sekvencijalni podaci se odlikuju po tome sto je:

    • Redosled nije bitan

    • Ne menjaju znacenje ako se permutuju

    • Redosled bitan

    Correct Answer
    A. Redosled bitan
    Explanation
    Sequential data is characterized by the fact that the order or sequence of the data is important. This means that changing the order of the data can alter its meaning or interpretation. Therefore, in this case, the correct answer is "Redosled bitan" which translates to "Order is important."

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  • 4. 

    Koeficijent korelacije meri:

    • Trigonometrijsku zavisnost

    • Linearnu zavisnost

    • Nelinearnu zavisnost

    Correct Answer
    A. Linearnu zavisnost
    Explanation
    The correct answer is "Linearnu zavisnost" which translates to "Linear dependence" in English. The coefficient of correlation measures the strength and direction of the linear relationship between two variables. It indicates how closely the data points in a scatter plot cluster around a straight line. A high correlation coefficient indicates a strong linear relationship, while a low correlation coefficient suggests a weak or no linear relationship. Therefore, the coefficient of correlation measures linear dependence between variables.

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  • 5. 

    Jednostavan model sa puno podataka za obucavanje je bolji od slozenijeg modela sa manje obucavajucih podataka:

    • Netacno

    • Nije moguce takvo poredjenje 

    • Tacno

    Correct Answer
    A. Tacno
    Explanation
    A simple model with a large amount of training data is better than a complex model with fewer training data because having more data allows the model to learn more effectively and make more accurate predictions. The simplicity of the model also helps in reducing overfitting and improves generalization. Therefore, the statement "Tacno" (True) is correct.

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  • 6. 

    Hijerarhijsko klasterovanje formira:

    • Skup disjunktnih klastera

    • Skup ugnezdenih klastera organizovanih u obliku drveta

    • Skup preklapajucih klastera

    Correct Answer
    A. Skup ugnezdenih klastera organizovanih u obliku drveta
    Explanation
    Hijerarhijsko klasterovanje formira skup ugnezdenih klastera organizovanih u obliku drveta. This means that the clusters are organized in a hierarchical structure, where each cluster is nested within another cluster, forming a tree-like structure. This allows for a clear understanding of the relationships and similarities between different clusters, as well as the ability to easily navigate and analyze the data.

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  • 7. 

    Skup reci u nekom dokumentu je:

    • Diskretan atribut

    • Kontinualan atribut

    • Niti kontinualan, niti diskretan

    Correct Answer
    A. Diskretan atribut
    Explanation
    The given correct answer is "Diskretan atribut". This means that the set of words in a document is a discrete attribute. A discrete attribute is a type of attribute that has a finite or countable number of distinct values. In this case, the set of words in a document can be counted and each word is distinct from the others. Therefore, it can be categorized as a discrete attribute.

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  • 8. 

    Hijerarhijsko klasterovanje se vizualizuje u oblliku:

    • Dendograma

    • Datagrama

    • Radiograma

    Correct Answer
    A. Dendograma
    Explanation
    Hijerarhijsko klasterovanje se vizualizuje u obliku dendograma. Dendogram je grafički prikaz hierarhijske strukture klastera, koji se sastoji od spojenih linija koje predstavljaju sličnost između klastera. Na x-osi se nalaze objekti koji se klasteruju, dok se na y-osi nalazi mera udaljenosti između klastera. Dendogram omogućava vizuelno prikazivanje procesa klasterovanja i identifikaciju grupa objekata koje su slične jedna drugoj.

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  • 9. 

    Da li je temperatura:

    • Niti je diskretan, niti kontinualan

    • Diskretan atribut

    • Kontinualan atribut

    Correct Answer
    A. Kontinualan atribut
    Explanation
    The given question is asking about the nature of temperature. The answer "Kontinualan atribut" suggests that temperature is a continuous attribute. This means that temperature can take on any value within a certain range and can be measured with precision. It is not limited to specific discrete values or categories.

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  • 10. 

    Klaster analiza je pronalazenje grupe objekata takvih da su:

    • Objekti u jednoj grupi slicni i da su istovremeno razliciti od objekata u drugim grupama

    • Objekti u razlicitim grupama razliciti

    • Objekti u jednoj grupi slicni

    Correct Answer
    A. Objekti u jednoj grupi slicni i da su istovremeno razliciti od objekata u drugim grupama
    Explanation
    The correct answer suggests that cluster analysis involves finding groups of objects that are similar within each group but different from objects in other groups. This means that objects within a cluster share similarities among themselves but are distinct from objects in other clusters.

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  • 11. 

    Kod asimetricnih atributa su jedino bitne:

    • Nulte vrednosti atributa

    • Negativne vrednosti atributa

    • Ne nulte vrednosti atributa

    • Pozitivne vrednosti atributa

    Correct Answer
    A. Ne nulte vrednosti atributa
    Explanation
    The correct answer is "Non-zero attribute values." This means that only the attribute values that are not equal to zero are important in this context.

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  • 12. 

    Dobar reprezentativni uzorak ima:

    • Aproksimativno iste osobine kao i originalni podaci

    • Znacajno se razlikuje po osobinama od originalnih podataka

    • Osobine reprezentativnog uzorka nemaju nikakve veze sa osobinama originalnih podataka

    Correct Answer
    A. Aproksimativno iste osobine kao i originalni podaci
    Explanation
    A good representative sample has approximately the same characteristics as the original data. This means that the sample accurately reflects the population from which it is drawn, allowing for valid inferences and generalizations to be made about the population based on the sample.

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  • 13. 

    Ako je g(X)=g_1_(X)-g_2_(X), gde su g_1_(X) i g_2_(X), diskiminacione funkcije za klasu 1 i klasu 2, respektivno, tada se odlucujemo za klasu 1, ako je ispunjeno:

    • G(X)=1

    • G(X)<0

    • G(X)>0

    Correct Answer
    A. G(X)>0
    Explanation
    The correct answer is "g(X)>0" because if the value of g(X) is greater than 0, it means that the discriminant function for class 1 is larger than the discriminant function for class 2. This indicates that the input X belongs to class 1, and therefore, we decide to classify it as class 1.

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  • 14. 

    ZKP (Zero Knowledge Protocols) ili protkol dokazivanja nultog znanja obezbedjuju:

    • Da jedna strana dokaze da poseduje neku tajnu i da je pri tom dokazivanju otkrije tajnu samo izabranom skupu entitija

    • Da jedna strana dokaze da poseduje neku tajnu i da je pri tom dokazivanju ne otkrije

    • Da jedna strana dokaze da poseduje neku tajnu i da je pri tom dokazivanju otkrije

    Correct Answer
    A. Da jedna strana dokaze da poseduje neku tajnu i da je pri tom dokazivanju ne otkrije
    Explanation
    The correct answer is "Da jedna strana dokaze da poseduje neku tajnu i da je pri tom dokazivanju ne otkrije." This means that the Zero Knowledge Protocols allow one party to prove that they possess a secret without revealing the secret itself during the proof.

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  • 15. 

    Redukcija dimenzija podataka po pravilu:

    • Smanjuje potrebu za dugackim obucavajucim skupovima

    • Povecava potrebu za dugackim obucavajucim skupovima

    • Olaksava vizualizaciju

    • Omogucava bolji rad algoritama masinskog ucenja

    • Ne utice na rad algoritama masinskog ucenja

    • Otezava vizualizaciju

    Correct Answer(s)
    A. Smanjuje potrebu za dugackim obucavajucim skupovima
    A. Olaksava vizualizaciju
    A. Omogucava bolji rad algoritama masinskog ucenja
    Explanation
    Dimensionality reduction reduces the need for long training sets because it reduces the number of features or variables in the data. It also facilitates visualization by reducing the data to a lower-dimensional space that can be easily visualized. Additionally, it enables better performance of machine learning algorithms as it removes irrelevant or redundant features, allowing the algorithms to focus on the most important information.

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  • 16. 

    Agregacija je operacija:

    • Eliminacije objekata

    • Kombinovanja dva ili vise atributa

    • Kombinovanja dva ili vise objekata

    • Eliminacije atributa

    Correct Answer(s)
    A. Kombinovanja dva ili vise atributa
    A. Kombinovanja dva ili vise objekata
    Explanation
    Aggregation is an operation that combines two or more attributes or objects. It is a process of creating a new entity by combining existing attributes or objects. In this case, the correct answer includes both options of combining two or more attributes and combining two or more objects.

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  • 17. 

    Izborom reprezentativnog uzorka se:

    • Broj podataka ostaje isti

    • Povecava broj podataka za obradu

    • Smanjuje broj podataka za obradu

    Correct Answer
    A. Smanjuje broj podataka za obradu
    Explanation
    By selecting a representative sample, the number of data to be processed is reduced. This is because a representative sample is a smaller subset of the entire population or dataset that accurately represents the characteristics and diversity of the whole. By analyzing this smaller sample, researchers can make inferences and draw conclusions about the larger population, saving time and resources compared to processing the entire dataset. Therefore, the correct answer is that selecting a representative sample reduces the number of data to be processed.

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  • 18. 

    Ako je mera razlicitosti izmedju dva vektora obelezja normirana na interval (0, 1), kako bi se opisala cinjenica da je mera razlicitosti izmedju dva vektora obelezja jednaka 1:

    • Ne vazi nijedan od gornjih stavova

    • Ta dva vektora obelezja su maksimlano razlicita

    • Ta dva vektora obelezja su identicna

    Correct Answer
    A. Ta dva vektora obelezja su maksimlano razlicita
    Explanation
    The given answer suggests that if the measure of dissimilarity between two feature vectors is equal to 1, it means that the two feature vectors are maximally different from each other.

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  • 19. 

    Da li optimalno Bajesovo odlucivanje u teorijskom smislu obezbedjuje minimalnu gresku odlucivanja: ???

    • Ne

    • Nekad da, nekad ne

    • Da

    Correct Answer
    A. Da
    Explanation
    Bajesovo odlučivanje u teorijskom smislu obezbeđuje minimalnu grešku odlučivanja. Bajesova teorija se zasniva na Bayesovom teoremu koji koristi statističke metode za donošenje odluka na osnovu raspoloživih podataka. Ova metoda uzima u obzir verovatnoću pojedinih događaja i koristi ih za procenu najbolje odluke. Kada se optimalno primeni, Bajesovo odlučivanje može minimizirati greške odlučivanja i obezbediti najbolji mogući rezultat.

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  • 20. 

    Prokletstvo dimenzija je pojava pri kojoj se:

    • Pri povecanju dimenzionalnosti podataka nuzno javlja njihovo proredjivanje u prostoru koji zauzimaju

    • Podaci udaljuju od koordinatnog pocetka

    • Podaci pomeraju ka koordinatnom pocetku

    • Pri povecanju dimenzionalnosti podataka nuzno javlja njihovo zgusnjavanje u prostoru koji zauzimaju

    Correct Answer
    A. Pri povecanju dimenzionalnosti podataka nuzno javlja njihovo proredjivanje u prostoru koji zauzimaju
    Explanation
    When the dimensionality of data increases, it is necessary for the data to become more sparse in the space they occupy. This means that the data points become more spread out and move away from the coordinate origin. As the dimensionality increases, the data points tend to become more scattered and spread out, leading to sparsity in the space they occupy.

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  • 21. 

    Bajesovo pravilo minimalne greske odlucivanja, kada poznajemo samo apriorne verovatnoce klasa glasi: 

    • Odlucujemo se za klasu omega_i, ukoliko je P(omega_i) > P(omega_j), za svako j, j razlicito od i

    • Odlucujemo se za klasu omega_i, ukoliko je P(omega_i) = P(omega_j), za svako j, j razlicito od i

    • Odlucujemo se za klasu omega_i, ukoliko je P(omega_i) < P(omega_j), za svako j, j razlicito od i

    Correct Answer
    A. Odlucujemo se za klasu omega_i, ukoliko je P(omega_i) > P(omega_j), za svako j, j razlicito od i
    Explanation
    The correct answer states that we choose the class omega_i if the probability P(omega_i) is greater than the probability P(omega_j) for every j, where j is different from i. This means that we select the class with the highest probability among all the classes.

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  • 22. 

    Ako su mere slicnosti izmedju vektora obelezja normirane na interval (0,1), cemu najbolje odgovara cinjenica da je mera slicnosti izmedju dva vektora obelezja jenaka 0:

    • Ta dva vektora su maksimalno razlicita

    • Nijedan od gornjih stavova

    • Ta dva vektora su identicna

    Correct Answer
    A. Ta dva vektora su maksimalno razlicita
    Explanation
    The correct answer suggests that when the similarity measure between two feature vectors is equal to 0, it indicates that the two vectors are maximally different. This means that there is no similarity or overlap between the features of the two vectors.

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  • 23. 

    Klasterovanje pomocu K-sredina je:

    • Mesovito klasterovanje

    • Hijerarhijsko klasterovanje

    • Particiono klasterovanje

    Correct Answer
    A. Particiono klasterovanje
    Explanation
    The correct answer is "Particiono klasterovanje." This refers to partition clustering, which involves dividing the data into non-overlapping subsets or partitions. Each partition represents a cluster, and each data point belongs to only one cluster. This method is different from hierarchical clustering, where clusters are formed based on a hierarchy of nested clusters, and from mixed clustering, which combines different types of clustering algorithms.

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  • 24. 

    Da li izbor pocetnih centroida u algoritmu K-sredina utice na konacno resenje?

    • Nekada utice, nekada ne utice

    • Utice

    • Ne utice

    Correct Answer
    A. Utice
    Explanation
    The correct answer is "Utice" which means "It does influence" in English. This implies that the choice of initial centroids in the K-means algorithm does have an impact on the final solution. The initial centroids determine the starting points for the clustering process, and different initial choices can lead to different cluster assignments and ultimately different final solutions.

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  • 25. 

    Redukcija dimenzija podataka po pravilu:

    • Smanjuje potrebu za dugackim obucavajucim skupovima

    • Povecava potrebu za dugackim obucavajucim skupovima

    • Olaksava vizualizaciju

    • Omogucava bolji rad algoritama masinskog ucenja

    • Ne utice na rad vecine algoritama masinskog ucenja

    • Otezava vizualizaciju

    Correct Answer(s)
    A. Smanjuje potrebu za dugackim obucavajucim skupovima
    A. Olaksava vizualizaciju
    A. Omogucava bolji rad algoritama masinskog ucenja
    Explanation
    Dimensionality reduction reduces the need for long training sets by reducing the number of features or variables in the data. This makes it easier to visualize the data as it reduces the complexity and allows for easier interpretation. Additionally, dimensionality reduction enables better performance of machine learning algorithms as it reduces noise and redundancy in the data, leading to improved accuracy and efficiency.

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  • 26. 

    U modelovanju klasifikacionog problema, smisao ekskluzivnosti klasa znaci:

    • Klase se preklapaju

    • Klase su multiplikativne

    • Klase se ne preklapaju

    • Klase su aditivne

    Correct Answer
    A. Klase se ne preklapaju
    Explanation
    The correct answer is "Klase se ne preklapaju" which translates to "Classes do not overlap" in English. This means that the classes in the classification problem are distinct and do not have any common elements. Each data point belongs to only one class and there is no ambiguity or overlap in assigning the data points to their respective classes.

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  • 27. 

    Sta je evidens?

    • P(X)

    • P(omega_i|X)

    • P(X|omega_i)

    Correct Answer
    A. P(X)
    Explanation
    The correct answer is P(X). In probability theory, P(X) represents the probability of event X occurring. It is a measure of the likelihood of X happening. In this context, "evidens" refers to the evidence or information available. Therefore, P(X) represents the probability of event X given the available evidence.

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  • 28. 

    Za podatke u Euklidskom prostoru, kao mera kvaliteta klasterovanja se koristi kriterijum:

    • Proizvod kvadrata gresaka

    • Zbir kvadrata gresaka

    • Razlika kvadrata gresaka

    Correct Answer
    A. Razlika kvadrata gresaka
    Explanation
    In the context of data clustering in Euclidean space, the measure of quality used is the criterion of the difference of squared errors. This means that the quality of the clustering is evaluated based on the difference between the actual values and the predicted values, with the errors being squared to emphasize the importance of larger errors. The larger the difference of squared errors, the worse the clustering quality, as it indicates a larger discrepancy between the predicted and actual values. Therefore, this criterion is used to assess the effectiveness of clustering algorithms in minimizing errors.

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  • 29. 

    Autlajeri su objekti koji su:

    • Znacajno razliciti od najveceg broja objekata u datom skupu podataka

    • Tipicni za dati skup podataka

    • Malo razliciti od najveceg broja objekata u datom skupu podataka

    Correct Answer
    A. Znacajno razliciti od najveceg broja objekata u datom skupu podataka
    Explanation
    Autlajeri su objekti koji su značajno različiti od najvećeg broja objekata u datom skupu podataka. This statement suggests that autlajeri are significantly different from the majority of objects in the given dataset.

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  • 30. 

    Ako nam je poznata minimalna Bajesova greska odlucivanja, ima li smisla tragati za pravilom odlucivanja koje daje jos manju gresku:

    • Ima

    • Nekad ima, nekad nema

    • Nema

    Correct Answer
    A. Nema
    Explanation
    The answer "Nema" suggests that it does not make sense to search for a decision rule that gives even smaller error than the known minimum Bayesian error. This implies that the minimum Bayesian error is already the best possible error rate that can be achieved, and there is no point in looking for a better decision rule.

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  • 31. 

    Da li je u hijerarhijskom klasterovanju neophodno unapred odrediti broj klastera?

    • Da

    • Ne

    Correct Answer
    A. Ne
    Explanation
    In hierarchical clustering, it is not necessary to predefine the number of clusters. This is because hierarchical clustering builds a tree-like structure (dendrogram) where the number of clusters can be determined by cutting the dendrogram at a desired height. This allows for flexibility in choosing the number of clusters based on the specific requirements of the analysis. Therefore, the correct answer is "No".

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  • 32. 

    Na koji nacin se nakon dobijanja dendograma u hijerarhijskom klasterovanju dobija zeljeni broj klastera?

    • Skracivanjem dendograma na odgovarajuci nivo

    • Reklasifikacijom

    • Eliminacijom uzoraka

    Correct Answer
    A. Skracivanjem dendograma na odgovarajuci nivo
    Explanation
    Nakon dobijanja dendograma u hijerarhijskom klasterovanju, zeljeni broj klastera se dobija skracivanjem dendograma na odgovarajući nivo. Ovo se može postići postavljanjem praga na odgovarajuću visinu u dendogramu, gde se presecaju grane koje predstavljaju klasterovanje. Na taj način se formiraju klasteri prema zadanom broju.

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  • 33. 

    Ako je diskriminaciona funkcija klase i data sa g_i_(x), i=1,2...,C, tada vazi:  ???

    • X pripada klasi j, ako je g_j_(x) > g_i_(x), za svako i=1,2..,C, i razlicito od j

    • X pripada klasi j, ako je g_j_(x) = g_i_(x), za svako i=1,2..,C, i razlicito od j

    • X pripada klasi j, ako je g_j_(x) < g_i_(x), za svako i=1,2..,C, i razlicito od j

    Correct Answer
    A. X pripada klasi j, ako je g_j_(x) > g_i_(x), za svako i=1,2..,C, i razlicito od j
    Explanation
    The correct answer states that x belongs to class j if g_j_(x) is greater than g_i_(x) for all i=1,2..,C, and different from j. This means that the discriminant function g_j_(x) for class j should have a higher value than the discriminant functions g_i_(x) for all other classes i, indicating that x is most likely to belong to class j.

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  • 34. 

    Na imenske atribute se moze primeniti operacija:

    • Razlicitosti

    • Aditivnosti

    • Multiplikativnosti

    • Uredjenja

    Correct Answer
    A. Razlicitosti
    Explanation
    Na imenske atribute se može primeniti operacija "Različitosti". Ova operacija se koristi za poređenje vrednosti dva ili više imenskih atributa kako bi se utvrdilo da li su oni različiti ili ne. Na primer, možemo uporediti vrednosti atributa "boja" i "veličina" da bismo videli da li su različiti.

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  • 35. 

    U svakom koraku rada razdvajajuceg hijerarhijskog klasterovanja:

    • Klasteri se dele

    • Prvo se podele, a zatim se objedine

    • Klasteri se objedinjavaju

    • Prvo se objedine, a zatim se dele

    Correct Answer
    A. Klasteri se dele
    Explanation
    In each step of divisive hierarchical clustering, clusters are divided. This means that the existing clusters are split into smaller clusters based on some criterion, such as distance or similarity. This process continues until each data point is assigned to its own individual cluster.

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  • 36. 

    Ako je za dati sistem klasifikacije ustanovljena minimalna Bajesova greska odlucivanja, na koji nacin mozemo redizajnirati sistem, tako da se njegova greska smanji ispod ove granice:

    • Promenom pravila odlucivanja

    • Promenom hardversko softverske realizacije sistema

    • Promenom obelezja

    Correct Answer
    A. Promenom pravila odlucivanja
    Explanation
    By changing the decision rules, we can redesign the system in a way that reduces its error below the established minimum Bayesian decision error. This means that by modifying the criteria or conditions used to make decisions within the classification system, we can improve its accuracy and reduce the likelihood of making incorrect classifications.

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  • 37. 

    Vizualizacija podataka je transformacija podataka:

    • Iz pocetne dimenzionalnosti N u dimenzionalnost 3, N>3

    • Iz pocetne dimenzionalnosti N u dimenzionalnost 2, N vece ili jednako 3

    • Iz pocetne dimenzionalnosti N u dimenzionalnost N+1, N>3

    • Iz pocetne dimenzionalnosti N u dimenzionalnost N-1, N>3

    Correct Answer(s)
    A. Iz pocetne dimenzionalnosti N u dimenzionalnost 3, N>3
    A. Iz pocetne dimenzionalnosti N u dimenzionalnost 2, N vece ili jednako 3
    Explanation
    The correct answer states that data visualization is a transformation of data from an initial dimensionality N to a dimensionality of 3, where N is greater than 3. It also states that data can be transformed from an initial dimensionality N to a dimensionality of 2, where N is greater than or equal to 3.

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  • 38. 

    Transakcioni podaci se odlikuju po tome sto svaki slog sadrzi:

    • Skup stavki

    • Bilo koju diskretnu vrednost

    • Jednu numericku vrednost

    Correct Answer
    A. Skup stavki
    Explanation
    The correct answer is "Skup stavki" because the given statement mentions that each record in transaction data contains a set of items. This implies that the transaction data is organized in such a way that multiple items are associated with each record.

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  • 39. 

    Sta je verodostojnost (likelihood) klase omega_i:

    • P(X|omega_i)

    • P(omega_i|X)

    • P(omega_i)

    Correct Answer
    A. P(X|omega_i)
    Explanation
    The given answer, P(X|omega_i), refers to the likelihood of class omega_i given the data X. This term represents the probability of observing the data X, given that the true class is omega_i. In other words, it quantifies how well the data X is explained by the class omega_i. It is an essential component in various statistical and machine learning algorithms, such as Naive Bayes and Maximum Likelihood Estimation, for estimating class probabilities and making predictions.

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  • 40. 

    Kod hijerarhijskog aglomerativnog klasterovanja se u svakom koraku:

    • Objedinjava par klastera najblizih prethodno objedinjenim klasterima

    • Objedinjava par najblizih klastera

    • Objedinjava par najudaljenijih klastera

    Correct Answer
    A. Objedinjava par najblizih klastera
    Explanation
    In each step of the hierarchical agglomerative clustering, the algorithm combines the pair of clusters that are closest to each other. This means that the algorithm starts with individual data points as clusters and then iteratively merges the two closest clusters until all data points belong to a single cluster. This approach allows the algorithm to create a hierarchy of clusters, where each level represents a different level of similarity or distance between the clusters.

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  • 41. 

    Agregirani podaci imaju tendenciju: 

    • Imaju veca odstupanja

    • Ostanu isti u pogledu odstupanja u odnosu na neagregirane podatke

    • Imaju manja odstupanja

    Correct Answer
    A. Imaju manja odstupanja
    Explanation
    Agregirani podaci imaju tendenciju da imaju manja odstupanja. This means that when data is aggregated, the variations or differences between the individual data points are reduced. Aggregation involves combining multiple data points into a single value or summary, which can help to smooth out any outliers or extreme values. Therefore, the aggregated data tends to have smaller deviations or discrepancies compared to the unaggregated data.

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  • 42. 

    Ako je skup objekata kojima raspolazemo mali, koji metod eliminacije nedostajucih vrednosti je najprihvatljiviji:

    • Zamena sa svim mogucim vrednostima, poredjanim tezinski prema verovatnoci pojavljivanja

    • Procena nedostajucih vrednosti

    • Eliminacija objekata

    Correct Answer
    A. Procena nedostajucih vrednosti
    Explanation
    When the set of objects we have is small, it is more reasonable to estimate the missing values rather than replacing them with all possible values or eliminating the objects. Estimating the missing values allows us to make educated guesses based on the available data, which can provide more accurate results compared to the other methods.

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  • 43. 

    Tipovi uzoraka su:

    • Izbor uzorka po slojevima (delovima)

    • Semideterminisani

    • Izbor uzorka sa zamenom

    • Preferencijalni

    • Jednostavni slucajni uzorak

    • Izbor uzorka bez zamene

    • Didakticki

    Correct Answer(s)
    A. Izbor uzorka po slojevima (delovima)
    A. Izbor uzorka sa zamenom
    A. Jednostavni slucajni uzorak
    A. Izbor uzorka bez zamene
    Explanation
    The correct answer includes different types of sampling techniques. "Izbor uzorka po slojevima (delovima)" refers to stratified sampling, which involves dividing the population into different strata or layers and then selecting a sample from each stratum. "Izbor uzorka sa zamenom" refers to random sampling with replacement, where each member of the population has an equal chance of being selected multiple times. "Jednostavni slucajni uzorak" refers to simple random sampling, where each member of the population has an equal chance of being selected. "Izbor uzorka bez zamene" refers to random sampling without replacement, where each member of the population can be selected only once. These different sampling techniques allow researchers to obtain representative and unbiased samples for their studies.

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  • 44. 

    Kod aglomerativnog hijerarhijskog klasterovanja, na pocetku rada je:

    • Svaki vektor obelezje je jedan klaster

    • Svi vektori su jedan klaster

    • Svaki drugi vektor obelezja je jedan klaster

    Correct Answer
    A. Svaki vektor obelezje je jedan klaster
    Explanation
    At the beginning of agglomerative hierarchical clustering, each vector feature is considered as a separate cluster. This means that initially, each vector is treated as its own cluster. As the clustering algorithm progresses, these individual clusters will be merged together based on their similarity until a final clustering solution is obtained.

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  • 45. 

    U kriterijumu sume kvadrata gresaka u klaster analizi, greska se odredjuje:

    • Kao rastojanje svakog vektora obelezja do centra najblizeg klastera

    • Kao rastojanje do vektora srednje vrednosti za sve raspolozive podatke

    • Kao rastojanje svakog vektora obelezja do centra najdaljeg klastera

    • Kao rastojanje do najblizeg vektora obelezja

    Correct Answer
    A. Kao rastojanje svakog vektora obelezja do centra najblizeg klastera
    Explanation
    In the criterion of sum of squared errors in cluster analysis, the error is determined as the distance of each feature vector to the center of the nearest cluster. This means that the error is calculated by measuring the distance between each data point and the centroid of the cluster it belongs to. The closer the data point is to the centroid, the smaller the error will be.

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  • 46. 

    Na redne atribute se moze primeniti operacija:

    • Multiplikativnosti

    • Aditivnosti

    • Uredjenja

    • Razlicitosti

    Correct Answer(s)
    A. Uredjenja
    A. Razlicitosti
    Explanation
    The correct answer is "Uredjenja, Razlicitosti." This means that the operation of ordering and the operation of distinctness can be applied to ordinal attributes. In other words, ordinal attributes can be arranged in a specific order and can have different values.

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  • 47. 

    U klasifikacionim problemima, smisao potpunosti klasa znaci:

    • Klase cine jedan partitivni skup u prostoru klasa

    • Klase su aditivne

    • Svaki uzorak koji podleze klasifikaciji mora pripadati jednoj od definisanih klasa

    • Klase su medjusobno nezavisne

    Correct Answer
    A. Svaki uzorak koji podleze klasifikaciji mora pripadati jednoj od definisanih klasa
    Explanation
    In classification problems, the concept of class completeness means that every sample that undergoes classification must belong to one of the defined classes. This implies that there should be no samples that do not fall into any class or belong to multiple classes. Class completeness ensures that all samples are properly categorized and accounted for in the classification process.

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  • 48. 

    Sta je generativni model jedne klase fenomena:

    • Funckija gustine raspodele smese klasa

    • Apriorne verovatnoce klase

    • Funkcija aposteriorne verovatnoce klase

    • Funkcija uslovne gustine raspodele klase

    Correct Answer
    A. Apriorne verovatnoce klase
    Explanation
    The correct answer is "Apriorne verovatnoce klase". This refers to the prior probabilities of each class in a generative model. These probabilities represent our knowledge or assumptions about the likelihood of each class occurring before seeing any data. They are used in calculating the posterior probabilities, which are updated probabilities after observing the data. In a generative model, these prior probabilities are used along with the likelihood function to estimate the class membership probabilities for new data points.

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  • 49. 

    Na razmerne atribute se moze primeniti operacija:

    • Aditivnosti

    • Multiplikativnosti

    • Razlicitosti

    • Uredjenja

    Correct Answer(s)
    A. Aditivnosti
    A. Multiplikativnosti
    A. Razlicitosti
    A. Uredjenja

Quiz Review Timeline (Updated): Mar 21, 2023 +

Our quizzes are rigorously reviewed, monitored and continuously updated by our expert board to maintain accuracy, relevance, and timeliness.

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  • Mar 21, 2023
    Quiz Edited by
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    Annwyn
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