Type of classification in which class is subdivided into sub classes and subclasses are divided into more classes is considered as 
Simple classification.
Manifold classification.
Rational classification.
Reflected classification.
Manifold classification.
What is not true about multi-dimensional visualisation?
Quick access to relevant business insights.
Disintegration of data and latest trends.
Sales analysis prediction figures.
None of the above.
Disintegration of data and latest trends.
Which of the following techniques would perform better for reducing dimension of a data set?
Remaining columns which have too many missing values.
Remaining columns with dissimilar data trends.
Removing columns which have high variance in data.
None of the above.
Remaining columns which have too many missing values.
The most popularly dimensionality reduction algorithm/technique is principal component analysis (PCA). Which of the following is/are true about PCA? I: PCA is supervised value. (II): It searches for the directions that data have the largest variance. (III): Maximum number of principal components is equal to number of features. (IV): All principal components are orthogonal to each other.
I and II
I, II and IV
II and III
All of the above
All of the above
Dimension Reduction techniques consists of: (I): Domain Knowledge (II): Data Exploration techniques (III): Data Conversion techniques (IV): Automated conversion techniques (V): Data mining techniques
II, IV and V
I, III and IV
I, IV and V
All of the above
All of the above
Consider the following image, which of the following is/are example of multi-collinear features:
Features in image 1
Features in image 3
Features in image 2&3
Features in image 1&2
Features in image 1&2
Adding a non-important feature to a linear regression model may result in -[ Statement I: Increase R-square ] [ Statement II: Decrease R-square ]
Only statement I is correct
Only statement II is correct
None of these
Either statement I or II
Only statement I is correct
Which of them is the best considered for prediction?
Linear regression
Logistic regression
CART
Naïve Bayes
Naïve Bayes
Correlation analysis is used to
Simultaneously compare the effect of multiple independent variables on a dependent variable.
Predict values of y based on value of x.
Measure the strength of association between two variables
All of the above
Measure the strength of association between two variables
The degree of linear association between two metric scaled variables is measured by
Pearson correlation coefficient
Significance level
Analysis of variance
β
Pearson correlation coefficient