Machine Learning Axioms | Fresco Play

Question 1: If you have a basket of different fruit varieties with some prior information on size, color, shape of each and every fruit . Which learning methodology is best applicable?

Answer: Supervised Learning

Question 2: Do you think heuristic for rule learning and heuristics for decision trees are both same ?

Answer: False

Question 3: What is the benefit of Naïve Bayes ?

Answer: Requires less training data

Question 4: What is the advantage of using an iterative algorithm like gradient descent ? (select the best)

Answer: For Nonlinear regression problems, there is no closed form solution

Question 5: For which one of these relationships could we use a regression analysis? Choose the correct one

Answer: Relationship between Height & weight (both Quantitative)

Question 6: Does Logistic regression check for the linear relationship between dependent and independent variables ?

Answer: False

Question 7: Which helps SVM to implement the algorithm in high dimensional space?

Answer: Kernel

Question 8: Kernel methods can be used for supervised and unsupervised problems

Answer: True

Question 9: Perceptron is _______________

Answer: a single layer feed-forward neural network

Question 10: While running the same algorithm multiple times, which algorithm produces same results?

Answer: Hierarchical clustering

Question 11: SVM will not perform well with large data set because (select the best answer)

Answer: Training time is high

Question 12: In a scenario, where the statistical model describes random error or noise instead of underlying relationship, what happens

Answer: Overfitting

Question 13: Consider a regression equation, Now which of the following could not be answered by regression?

Answer: Estimate whether the association is linear or non-linear

Question 14: Now Can you make quick guess where Decision tree will fall into _____

Answer: Supervised Learning

Question 15: The main difficulty with using a regression line to analyze these data is _________________

Answer: presence of 1 or more outliers

Question 16: For which one of these relationships could we use a regression analysis? Chose the correct one

Answer: Relationship between Height & weight (both Quantitative)

Question 17: The correlation between two variables is given by r = 0.0. . This means

Answer: The best straight line through the data is horizontal

Question 18: Which of the following is not example of Clustering?

Answer: RFM Analysis

Question 19: Most famous technique used in Text mining is

Answer: Naive Bayes

Question 20: Disadvantage of Neural network according to your purview is

Answer: takes long time to be trained

Question 21: One has to run through ALL the samples in your training set to do a single update for a parameter in a particular iteration. This is applicable for

Answer: Gradient Descent

Question 22: Which type of the clustering could handle Big Data?

Answer: K Means clustering

Question 23: Effect of outlier on the correlation coefficient ______________

Answer: An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to the other points

Question 24: If the outcome is continuous, which model to be applied?

Answer: Linear Regression

Question 25: SVM uses which method for pattern analysis in High dimensional space?

Answer: Kernel

Question 26: The model which is widely used for the classification is

Answer: Logistic Regression

Question 27: Objective of unsupervised data covers all these aspect except

Answer: low-dimensional representations of the data , find clusters of the data , detect interesting coordinates and correlations

Question 28: Correlation and regression are concerned with the relationship between _________

Answer: 2 quantitative variables

Question 29: Which model helps SVM to implement the algorithm in high dimensional space?

Answer: Kernel

Question 30: In Kernel trick method, We do not need the coordinates of the data in the feature space

Answer: True

Question 31: What are different types of Supervised learning

Answer: regression and classification

Question 32: Which methodology works with clear margins of separation points?

Answer: Support Vector Machine

Question 33: Which of the learning methodology applies conditional probability of all the variables with respective the dependent variable?

Answer: Supervised Learning

Question 34: The main problem with using single regression line

Answer: presence of 1 or more outliers

Question 35: What are the advantages of neural networks (i) ability to learn by example (ii) fault tolerant (iii) suited for real time operation due to their high 'computational' rates

Answer: All the options are correct

Question 36: Which clustering technique requires prior knowledge of the number of clusters required?

Answer: K Means clustering

Question 37: Which technique implicitly defines the class of possible patterns by introducing a notion of similarity between data?

Answer: Kernel

Question 38: Which of them, best represents the property of Kernel?

Answer: Modularity

Question 39: The model in which one estimates the probability that the outcome variable assumes a certain value, rather than estimating the value itself.

Answer: Logistic Regression

Question 40: If the outcome is binary(0/1), which model to be applied?

Answer: Logistic Regression

Question 41: SVM will not perform well with data with more noise because (select the best answer)

Answer: target classes could overlap

Question 42: The standard approach to supervised learning is to split the set of example into the training set and the test

Answer: True