Data Mining Quiz 4 Answers
Sufficient Number of output nodes required in an ANN used for two-class classification problem is:
Answer: 1
How are the weights and biases initialized in an ANN in general?
Can be initialized randomly
In which neural network, the links may connect nodes within the same layer or nodes from one layer to the previous layers?
Recurrent neural network
Neural Networks are complex ______________ with many parameters.
Nonlinear Functions
Artificial neural network used for:
a. Pattern Recognition b. Classification c. Clustering
A neuron with 3 inputs has the weight vector [0.2 -0.1 0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4 0.2]^T then the total input to the neuron is:
0.02
Under which of the following situation would you expect overfitting to happen?
With training iterations error on training set decreases but test set increases
Which of the following statement is NOT true about clustering? a) It is a supervised learning technique b) It is an unsupervised learning technique
a) It is a supervised learning technique
Which clustering technique start with the points as individual clusters and, at each step, merge the closest pair of clusters
Agglomerative clustering
DBSCAN is a___________ algorithm
Partitional clustering
The leaves of a dendogram in hierarchical clustering represent?
Individual data points
Distance between two clusters in complete linkage clustering is defined as:
Distance between the furthest pair of points between the clusters
Consider a set of five 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), and p5=(0, 0.5). Euclide-an distance is the distance function. Single linkage clustering is used to cluster the points into two clusters. The clusters are:
{p1, p4, p5} {p2, p3}
Consider a set of five 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), and p5=(0, 0.5). Euclide-an distance is the distance function. Complete linkage clustering is used to cluster the points into two clus-ters. The clusters are:
{p1, p4, p5} {p2, p3}
Consider a set of five 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), and p5=(0, 0.5). Euclidean distance is the distance function. The k-means algorithm is used to cluster the points into two clusters. The initial cluster centers are p1 and p5. The clusters after two iterations of k-means are:
{p1, p4, p5} {p2, p3}
Given a set of seven 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), p5=(0, 0.5), p6=(0, 9), and p7=(5.5, 1). Euclidean distance is the distance function. The DBSCAN algorithm is used to cluster the points. Epsilon = 1, and MinPts = 2 is used for DBSCAN. The clusters and outliers obtained are:
Clusters: {p1, p4, p5} {p2, p3, p7}; Outlier: p6
Target variable in regression is continuous or discrete?
Continuous
Regression is used in:
Predictive data mining
Regression finds out the model parameters which produces the least square error between
Output value and Target value
A time series prediction problem is often solved using?
Autoregression
In principal component analysis, the projected lower dimensional space corresponds to
Eigenvectors of the data covariance matrix
Total Number of Questions: 21
1000+ Students are taking advantage of instant notification, Join us on telegram.
Also Available
Leave a comment