A nearest neighbor approach is best used
1.with large-sized datasets.
2.when irrelevant attributes have been removed from the data.
3.when a generalized model of the data is desirable.
4.when an explanation of what has been found is of primary importance.
During the last few years, many ______ algorithms have been applied to deepneural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.
1.Logical
2.Classical
3.Classification
4.None of the above
How it's possible to use a different placeholder through the parameter_______.
1.regression
2.classification
3.random_state
4.missing_values
If Linear regression model perfectly first i.e., train error is zero, then _____________________
1.Test error is also always zero
2.Test error is non zero
3.Couldn’t comment on Test error
4.Test error is equal to Train error
if there is only a discrete number of possible outcomes (called categories),the process becomes a______.
1.Regression
2.Classification
3.Modelfree
4.Categories
If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class________
1. RobustScaler .
2.DictVectorizer
3.LabelBinarizer
4.FeatureHasher
In many classification problems, the target ______ is made up of categorical labels which cannot immediately be processed by any algorithm.
1.random_state
2.dataset
3.test_size
4.All of the above
In syntax of linear model lm(formula,data,..), data refers to ______
1.Matrix
2.Vector
3.Array
4.List
Logistic regression is a ________ regression technique that is used to model data having a_____outcome.
1. linear, numeric
2. linear, binary
3.nonlinear, numeric
4.nonlinear, binary
SVM is a ------------------ learning
1.Supervised
2.Unsupervised
3.Both
4.None
Techniques involve the usage of both labeled and unlabeled data is called___.
1.Supervised
2.Semi-supervised
3.unsupervised
4.None of the above
The leaf nodes of a model tree are
1.averages of numeric output attribute values.
2.nonlinear regression equations.
3. linear regression equations.
4.sums of numeric output attribute values.
This supervised learning technique can process both numeric and categorical input attributes.
1.linear regression
2.bayes classifier
3.logistic regression
4. backpropagation learning
What is ‘Training set’?
1.Training set is used to test the accuracy of the hypotheses generated by the learner.
2.A set of data is used to discover the potentially predictive relationship.
3.Both A & B
4.None of the above
Which of the following are supervised learning applications
1.Spam detection,Pattern detection,Natural Language Processing
2.Image classification,Real-time visual tracking
3.Autonomous car driving,Logistic optimization
4.Bioinformatics,Speech recognition
Which of the following is a common use of unsupervised clustering?
1.detect outliers
2.determine a best set of input attributes for supervised learning
3.evaluate the likely performance of a supervised learner model
4.determine if meaningful relationships can be found in a dataset
Which statement is true about prediction problems?
1. the output attribute must be categorical.
2.the output attribute must be numeric.
3. the resultant model is designed to determine future outcomes.
4. the resultant model is designed to classify current behavior.
A measure of goodness of fit for the estimated regression equation is the
1.multiple coefficient of determination
2.mean square due to error
3. mean square due to regression
4.None of these
A multiple regression model has
1.only one independent variable
2.more than one dependent variable
3.more than one independent variable
4.None of the above
A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2constant), y will
1.increase by 3 units
2.decrease by 3 units
3. increase by 4 units
4.decrease by 4 units
A regression model in which more than one independent variable is used to predict the dependent variable is called
1.a simple linear regression model
2.a multiple regression models
3.an independent model
4. none of the above
A supervised scenario is characterized by the concept of a _____.
1.Programmer
2.Teacher
3.Author
4.Farmer
A term used to describe the case when the independent variables in a multiple regression modelare correlated is
1.regression
2.correlation
3.multicollinearity
4.None of these
According to____ , it’s a key success factor for the survival and evolution of all species.
1.Claude Shannons theory
2. Gini Index
3.Darwin’s theory
4. None of above
Another name for an output attribute.
1.predictive variable
2.independent variable
3. estimated variable
4.dependent variable
Bernoulli Naïve Bayes Classifier is ___________distribution
1.Continuous
2.Discrete
3.Binary
4.None of these
Bootstrapping allows us to
1.choose the same training instance several times
2.choose the same test set instance several times.
3. build models with alternative subsets of the training data several times.
4.test a model with alternative subsets of the test data several times.
Classification problems are distinguished from estimation problems in that
1.classification problems require the output attribute to be numeric.
2.classification problems require the output attribute to be categorical.
3.classification problems do not allow an output attribute.
4. classification problems are designed to predict future outcome.
Common deep learning applications include____
1.Image classification,Real-time visual tracking
2.Autonomous car driving,Logistic optimization
3.Bioinformatics,Speech recognition
4.All of the above
Computers are best at learning
1.facts
2.concepts
3.procedures
4.principles
Data used to build a data mining model.
1.validation data
2.training data
3.test data
4.hidden data
Data used to optimize the parameter settings of a supervised learner model.
1.training
2.test
3.verification
4.validation
Even if there are no actual supervisors ________ learning is also based on feedback provided by the environment
1.Supervised
2.Reinforcement
3.Unsupervised
4.None of the above
Features being classified is __________ of each other in Naïve Bayes Classifier
1.Independent
2.Dependent
3.Partial Dependent
4.None
For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient ofdetermination is
1.0.25
2.4.00
3.0.75
4.None of the above
Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the _______ of the feature values.
1.Mean B. C. Discrete D.
2.Variance
3.Discrete
4.Random
In reinforcement learning if feedback is negative one it is defined as____.
1.Penalty
2.Overlearning
3.Reward
4.None of the above
Multinomial Naïve Bayes Classifier is ___________distribution
1.Continuous
2.Discrete
3.Binary
4.None of These
Naive Bayes classifiers are a collection ------------------of algorithms
1.Classification
2.Clustering
3.Regression
4.All of the above
Naive Bayes classifiers is _______________ Learning
1.Supervised
2.Unsupervised
3.Both
4.None
Regression trees are often used to model _______ data.
1.linear
2.nonlinear
3.categorical
4.symmetrical
Reinforcement learning is particularly efficient when______________.
1.the environment is not completely deterministic
2.its often very dynamic
3. its impossible to have a precise error measure
4.All of the above
scikit-learn also provides a class for per-sample normalization, Normalizer. It can apply________to each element of a dataset
1. max, l0 and l1 norms
2.max, l1 and l2 norms
3.max, l2 and l3 norms
4.max, l3 and l4 norms
Selecting data so as to assure that each class is properly represented in both the training andtest set.
1. cross validation
2.stratification
3.verification
4.bootstrapping
Simple regression assumes a __________ relationship between the input attribute and outputattribute.
1.linear
2.quadratic
3.reciprocal
4.inverse
Supervised learning and unsupervised clustering both require at least one
1.hidden attribute.
2.output attribute.
3.input attribute
4.categorical attribute.
Supervised learning differs from unsupervised clustering in that supervised learning requires
1.at least one input attribute.
2. input attributes to be categorical
3.at least one output attribute
4.output attributes to be categorical.
SVM is a ------------------ algorithm
1.Classification
2.Clustering
3.Regression
4.All
The adjusted multiple coefficient of determination accounts for
1. the number of dependent variables in the model
2.the number of independent variables in the model
3.unusually large predictors
4.None of the above
The average positive difference between computed and desired outcome values.
1.root mean squared error
2.mean squared error
3.mean absolute error
4.mean positive error
The average squared difference between classifier predicted output and actual output.
1.mean squared error
2.root mean squared error
3.mean absolute error
4. mean relative error
The effectiveness of an SVM depends upon:
1.selection of kernel
2.kernel parameters
3.soft margin parameter c
4.All of the above
The multiple coefficient of determination is computed by
1.dividing ssr by sst
2.dividing sst by ssr
3.dividing sst by sse
4.None of the above
The parameter______ allows specifying the percentage of elements to put into the test/training set
1.. test_size
2.training_size
3.All above
4.None of these
The process of forming general concept definitions from examples of concepts to belearned.
1.deduction
2.abduction
3.induction
4.conjunction
The standard error is defined as the square root of this computation.
1.the sample variance divided by the total number of sample instances.
2. the population variance divided by the total number of sample instances.
3.the sample variance divided by the sample mean.
4.the population variance divided by the sample mean.
There are also many univariate methods that can be used in order to select the best features according to specific criteria based on________.
1. F-tests and p-values
2.chi-square
3.ANOVA
4.All of the above
This clustering algorithm initially assumes that each data instance represents a single cluster.
1.agglomerative clustering
2. conceptual clustering
3. k-means clustering
4.expectation maximization
This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.
1.agglomerative clustering B.
2.expectation maximization
3. conceptual clustering
4.k-means clustering
This technique associates a conditional probability value with each data instance.
1.linear regression
2.logistic regression
3.simple regression
4.multiple linear regression
This unsupervised clustering algorithm terminates when mean values computed for the currentiteration of the algorithm are identical to the computed mean values for the previous iteration.
1. agglomerative clustering
2.conceptual clustering
3.k-means clustering
4.expectation maximization
When it is necessary to allow the model to develop a generalization ability and avoid a common problem called______.
1.Overfitting B. C. D. Regression
2.Overlearning
3.Classification
4.Regression
Which of the following is true about Residuals ?
1.Lower is better
2.Higher is better
3.A or B depend on the situation
4.None of These
Which of the following methods do we use to find the best fit line for data in Linear Regression?
1.Least Square Error
2.Maximum Likelihood
3.Logarithmic Loss
4.Both A and B
With Bayes classifier, missing data items are
1.treated as equal compares.
2.treated as unequal compares.
3.replaced with a default value
4. ignored.
____performs a PCA with non-linearly separable data sets.
1.SparsePCA
2.KernelPCA
3.SVD
4.None of the Mentioned
_____is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value
1.Removing the whole line
2. Creating sub-model to predict those features
3. Using an automatic strategy to input them according to the other known values
4.All of the above