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RapidMinder Machine Learning Professional Certification Quiz Exam Answers

Topic: Auto Model

  • is able to use GPU processors in your computer to speed up the modeling process.
  • encourages users to do feature selection which is often overlooked.
  • follows many data science ‘best practices’.
  • uses modeling algorithms that are not available as individual operators.
  • Generalized Linear Model
  • Deep Learning
  • Gradient Boosted Trees
  • Support Vector Machine
  • Naïve Bayes
  • Logistic Regression
  • Decision Tree
  • Fast Large Margin

Topic: Unsupervised Techniques

  • iteratively improving the position of k centroids in the sample space until an optimal placement is found.
  • starting with one point in the sample space, finding more points in the space within a neighborhood ℇ until no more points can be found, and then repeating this process for k-1 points.
  • iteratively determining the Gaussian distribution (via its mean and standard deviation) of k clusters until the probabilities of all points in the sample space are maximized.
  • pairing each point with another point such that their distance is minimized, and then repeating this process with larger groups of points until there are only k clusters remaining.
  • attributes a2 and a4 partition the data set well between cluster_0 and cluster_1.
  • attributes a1 and a3 do not partition the data set well between cluster_0 and cluster_1.
  • attributes a2 and a4 partition the data set well between cluster_0 and cluster_2.
  • attributes a2 and a4 do not partition the data set between cluster_0 and cluster_2.
  • ensure that a regression model is not overfitting the data.
  • find attributes that may have a relationship to one another.
  • eliminate data that do not fit a particular model.
  • computing the accuracy of a linear regression model.
  • Wind
  • Play
  • Outlook
  • Temperature
  • Humidity
  • Decision Tree
  • k-Means clustering
  • Support Vector Machine
  • FP-Growth
  • If you are not sure, then use the default value, 5. It is almost always optimal.
  • Start with X-Means instead of k-Means; it will find an optimal k according to a heuristic.
  • Start with a value of k that is large relative to the number of attributes that you have and apply k-Means. Then visualize the results with a scatter plot and set k to the number of distinct clusters.
  • There is no method that is consistent across all applications.
  • Year 1
  • Year 2
  • Year 3
  • Year 4
  • Year 5

Topic: Classification & Regression

  • The bias and variance both increase.
  • The bias and variance both decrease.
  • The bias increases and the variance decreases.
  • The bias decreases and the variance increases.
  • the attributes individually follow a Gaussian conditional probability distribution, given the class.
  • the attributes individually follow a Gaussian probability distribution, independent of the class.
  • the value of any attribute is statistically independent of the value of any other attribute (given the class value).
  • the value of any attribute is statistically dependent of the value of any other attribute (given the class value).
  • The model will likely overfit the data.
  • Building the model will likely take a very long time on a standard laptop.
  • The model will likely be too complex to interpret by humans.
  • Building the model will require multiple GPU processors installed on a large server.
  • For which examples will the model predict “yes”? (Select ALL correct answers)
  • Outlook=rain, Wind=true, Humidity=60
  • Outlook=overcast, Wind=false, Humidity=90
  • Outlook=sunny, Wind=true, Humidity=60
  • none of the examples above predict ‘true’
  • you have a numerical label and numerical attributes.
  • you have a binominal label and numerical attributes.
  • you have a numerical label and polynominal attributes.
  • the data is from a logistics use case.
  • increasing the number of training cycles
  • increasing the learning rate
  • increasing the momentum
  • adding more hidden layers
  • GLM
  • Naïve Bayes
  • k-NN
  • Decision Tree
  • you have polynominal attributes with many values.
  • you need to get the fastest runtime (Gain Ratio always has a shorter runtime than Information Gain).
  • you have a relatively small data set (they will both take similar time to run but Gain Ratio always gives better performance over Information Gain).
  • you want a criterion that takes Information Gain, and adjusts it for each attribute based on the number of possible values.
  • This model had 67 false positive predictions.
  • This model had 67 false negative predictions.
  • This model was able to correctly predict 705 “BAD” values out of a total of 772 “BAD” values in the ExampleSet.
  • Data scientists would consider this a ‘balanced’ data set.
  • Linear Regression
  • Naive Bayes
  • k-NN
  • GLM

Topic: Validation & Scoring

  • Label 1 points to the training set wire.
  • Label 1 points to the testing set wire.
  • Operator 2 is the operator that builds the model (e.g. Decision Tree, SVM, etc…)
  • Operator 3 is the operator that builds the model (e.g. Decision Tree, SVM, etc…)
  • ranking the performances of more than one model to choose the best one.
  • applying a model to unseen data.
  • using a model in production.
  • determining whether or not a model is overfit.

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