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SA4
| Question | Answer |
|---|---|
| In ID3, Entropy is calculated for every attribute. | True |
| ID3 selects a best attribute that yields maximum Entropy. | False |
| In Decision Tree, the leaf represents an outcome. | True |
| ID3 is an unsupervised learning algorithm. | False |
| ID3 is a supervised learning algorithm. | True |
| The _____ tells us how much uncertainty in S was reduced after splitting set S on attribute A. | GAIN |
| Successor of ID3. | C4.5 |
| The ID3 is a ________ algorithm. | Classification |
| Naïve Bayes is a ______ technique with an assumption of independence among predictors. | CLASSIFICATION |
| Neural Network is also referred to as ANN, where ‘A’ stands for _______. | ARTIFICIAL |
| Type of Neural Network where there is no back feedback to improve the nodes in different layers and not much self-learning mechanism. | FEED-FORWARD |
| Three layers of Neural Networks : Input, _______, Output. | HIDDEN |
| Naïve Bayes Algorithm is a classification technique based on Bayes’ Theorem with an assumption of _______________ among predictors. | INDEPENDENCE |
| In Apriori, the _______ is the conditional probability of some item, given you have certain other items in your itemset. | CONFIDENCE |
| The _______ closure property is an Apriori principle which means that All subset of any frequent itemset must also be frequent. | DOWNWARD |
| In Apriori, the ______ is the number of transactions containing the itemset divided by the total number of transactions. | SUPPORT |
| K-means is an ________ learning algorithm. | UNSUPERVISED |
| The ______ is the number of transactions containing the itemset divided by the total number of transactions. | SUPPORT |
| In decision tree, splitting means removing of sub-nodes of a decision node. | False |
| In ID3 algorithm, the first step is to compute for entropy of the dataset. | True |
| In Decision Tree, the node represents an attribute. | True |
| In decision tree, the ______ is the process of dividing a node intro two or more sub-nodes. | SPLITTING |
| In calculating the ______ of the entire data set, we need to calculate the number of positive and negative evidences. | ENTROPY |
| ID3 selects a best attribute that yields ________ Entropy. | MINIMUM |
| The ______ algorithm is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks. | BAYES/NAIVE BAYES |
| The ______ is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. | NEURAL NETWORK |
| The ____ is a type of Neural Network with more than one hidden layer. | MULTILAYER PERCEPTRON |
| The Truck Brake system diagnosis, vehicle scheduling, routing systems are applications of neural network in: _______. | Transportation |
| K-means is a ____________ algorithm technique. | CLUSTERING |
| A ________ rule is a data mining technique for learning correlations and relations among variables in a database. | ASSOCIATION |
| In Apriori, the _________ is the relative number of transactions which contains an itemset relative to the total transactions. | Relative support |
| The RBF is a type of Neural Networks that stands for: _______ Basis Function. | RADIAL |
| Neural Network is also referred to as ________. | ANN |
| Naïve Bayes Algorithm is a _______ technique based on Bayes’ Theorem with an assumption of independence among predictors. | CLASSIFICATION |
| Apriori 3-step approach: Join, ______, Repeat. | PRUNE |
| In K-means, we must randomly select k ______ from the data set as the initial cluster centroids. | Data points |
| In Apriori, we need to define first the ______ of itemset. | SIZE |
| K-means is a popular ___________ analysis technique for exploring a dataset. | CLUSTERING |
| Apriori is an ________ algorithm. | ASSOCIATION |
| A Regression Tree is a type of DT where the decision variable is Categorical. | False |
| In Decision Tree, the branch represents an outcome. | False |
| A classification tree is a type of DT where the decision variable is _________. | CATEGORICAL |
| The ______ is is the measure of the amount of uncertainty or randomness in data. | ENTROPY |
| The _____ tells us how much uncertainty in S was reduced after splitting set S on attribute A. | Information Gain |
| The entropy is is the measure of the amount of ______ or randomness in data. | UNCERTAINTY |
| Text Classification and Categorization is a Neural Network in ______. | Language |
| The Bayes Theorem allows us to predict the class given a set of features using _______. | PROBABILITY |
| K-means picks points in multi-dimensional space to represent each of the K clusters. These are called __________. | CENTROIDS |
| To compute for the relative support: total number of ______ containing an itemset X / total number of transaction. | TRANSACTIONS |
| In Apriori, the second element we need to define is the ______of the itemset. | SUPPORT |
| In Apriori, the third element we need to define is the ______of the itemset. | CONFIDENCE |
| The _____ is the conditional probability of some item given you have certain other items in your itemset. | CONFIDENCE |
| The attribute with highest entropy will be chosen as node. | False |
| In calculating the Entropy of the entire data set, we need to calculate the number of positive and negative evidences. | True |
| What is the formula in calculating information gain (IG)? | Gain = Entropy(S) – I(Attribute) |
| In the formula P(Class A|Feature 1, Feature 2), P stands for __________. | PROBABILITY |
| In the given set below, which row is a class? Row 1 = LION, DOG, ELEPHANT, GIRAFFE Row 2 = BIG, HEAVY, BROWN, BLACK | ROW 1 |
| The main intuition in these types of neural networks is the distance of data points with respect to the center. | RADIAL BASIS FUNCTION |
| In Apriori, the _______ is an itemset that meets the support. | FREQUENT/FREQUENT ITEMSET |
| In Apriori, the _____ step scans the whole database for how frequent 1-itemsets are. | JOIN |
| Apriori learns _______ rules and is applied to a database containing a large number of transactions. | ASSOCIATION |
| In decision tree, the _____ represents the entire population or sample and this further gets divided into two or more homogeneous sets. | ROOT NODE |
| A type of DT where the decision variable is Categorical. | CLASSIFICATION TREE |
| In ID3 algorithm, the first step is to compute for:________. | Entropy of dataset |
| The attribute with ______________ will be chosen as node. | Highest Gain Attribute |
| Speech recognition is a Neural Network in ______. | Signals |
| This type of neural network is an advanced version of Multilayer Perceptron. | CONVOLUTIONAL |
| The Automobile Guidance Systems is an application of neural network in: _______. | Automotive |
| Apriori 3-step approach: Join, Prune, _______. | REPEAT |
| In Apriori, we need to define first the _____of the itemset. | SIZE |
| In Naïve Bayes, the dataset is divided into two parts: feature matrix and ________. | RESPONSE VECTOR |
| The fundamental Naive Bayes assumption is that each feature makes an: independent and _____ contribution to the outcome. | EQUAL |
| An itemset is considered _______ if its support is no less than “minimum support threshold”. | FREQUENT |
| A terminal node is a note with no split. | True |
| In decision tree, a parent node is a node which is divided into sub-nodes. | True |
| In ID3, Entropy is calculated only at the root node. | False |
| The attribute with highest gain attribute will be chosen as node. | True |
| In ID3 algorithm, the first step is to compute for _______ of the dataset. | ENTROPY |
| ID3 is a : _______________ algorithm. | SUPERVISED LEARNING |
| Type of Neural Network with more than one hidden layer. | MULTILAYER PERCEPTRON |
| Apriori approach where itemsets that satisfy the supportand confidence move onto the next round for 2-itemsets. | PRUNE |
| In K-means algorithm, the first step is to choose a value of k number of _____ to be formed. | CLUSTERS |
| The _____ Theorem allows us to predict the class given a set of features using probability. | BAYES |
| The network with more than one hidden layer is called ______________. | Multilayer Perceptron |
| A Classification Tree is a type of DT where the decision variable is Categorical. | True |
| Character recognition is a Neural Network in _____. | Images |
| The ______ means that independently functioning different networks carry out sub-tasks. | MODULARITY |
| In Decision Tree, the leaf represents an attribute. | False |
Created by:
Sangunius