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MINE 272 final exam review(updated)questions & answers
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MINE 272 final exam review(updated)questions & answers
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MINE 272 final examreview(updated)questions & answers
Data analysis process - correct answer ✔✔1. Define problem
2. Data extraction
3. Data preparation
4. Data exploration and visualization
5. Predictive modelling, validation, and testing
Data preparation - correct answer ✔✔Step 3, cleaning and transformation
Transform input to new space to make problem easier to solve
Ex. PCA, scaling, log values
Data exploration and visualization - correct answer ✔✔Step 4, pattern recognition
Present data graphically and statistically to find patterns and connections
Ex. summarizing, grouping, relations between attributes
Predictive modelling, validation, and testing - correct answer ✔✔Step 5, selecting and evaluating model
Choose suitable statistical model for encoding relations, model then validated and tested (partition data)
Ex. regression, classification, clustering
,Dataset - correct answer ✔✔Collection of data composed of attributes and a response variable
Should be divided into testing and training datasets
Training set - correct answer ✔✔Tune parameters of adaptive model, inputs can be limited
Testing set - correct answer ✔✔Use trained model to predict target values of other points
A good predictor is well-generalized through pattern recognition
Supervised learning - correct answer ✔✔Training data has input with a corresponding target vector as
output
Ex. classification, regression (linear, logistic)
Classification - correct answer ✔✔Assigning input vectors to a finite number of discrete categories
(supervised learning)
Ex. rock type from an image, digit recognition
Regression - correct answer ✔✔Explains influence of variables on outcome of another variable
Output of a regression model is one or more continuous variables (supervised learning)
Ex. predicting real CO concentration, yield of chemical production plant (inputs T and P)
Linear regression - correct answer ✔✔Predict function returns prediction y (output) of a given x value
(input), based on linear regression model, analytical technique
Logistic regression - correct answer ✔✔Output is not continuous variable, binary outcome
, Based on logistic function where f(y) = e^y/1 + e^y for -infinity < y < +infinity
Unsupervised learning - correct answer ✔✔Training data does not have inputs with corresponding target
vectors as output
Ex. clustering (K-means clustering), density estimation
Clustering - correct answer ✔✔Discovers groups of similar examples within data, infinite number of
attributes (unsupervised learning)
Ex. ore grades of orebody
Density estimation - correct answer ✔✔Determine distribution of data within given input space,
converting high dimension data to 1D or 2D (unsupervised learning)
K-means clustering - correct answer ✔✔Finds k clusters in dataset with any number of attributes
(unsupervised learning)
Centroid = cluster mean
1. Define K, guess location of centroid
2. Compute the distance between the point and centroid (assign point closest centroid)
3. Compute centroid of each cluster
4. Repeat 2 and 3 until convergence (centroid stationary or oscillates)
WSS (within sum of squares) - if k+1 doesn't change WSS, number of k clusters is suitable
Time series - correct answer ✔✔Observations taken over time
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