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two word phrases are known as bigrams how can coding text with bigrams improve insights derived from text mining what
consider the following three snippets of text the rain in spain falls mainly in the plain the spanish world cup team is
modify the network in figure so that node 1 is now also linked to node 3 and node 5 and node 2 is now also linked to
1 clearly describe what is meant by classification2 using the classify risk data set with predictors age marital status
1 discuss the advantages and drawbacks of using a small value versus a large value for k2 why would one consider
1 what is the sole function of the nodes in the input layer2 should we prefer a large hidden layer or a small one
use the data set churn normalize the numerical data recode the categorical variables and deal with the correlated
browse your model in the network window of the model tab select the style coefficients record the pred1-to-neuron1
1 extra credit investigate the mixture idea for the continuous predictor mentioned in the text2 explain what is meant
1 explain why the log posterior odds ratio is useful provide an example2 describe the process for using continuous
1 when is the naiumlve bayes classification the same as the map classification what does this mean for the naiumlve
1 suppose our model has perfect sensitivity and perfect specificity what then is our accuracy and overall error rate2
1 true or false if model a has better accuracy than model b then model a has fewer false negatives than model b if
1 what is the difference between the total predicted negative and the total actually negative2 what is the relationship
1 why do we not use the average deviation as a model evaluation measure2 how is the square root of the mse interpreted3
use the breast cancer data set10 this data set was collected by dr william h wohlberg from the university of wisconsin
perform a costbenefit analysis for the default cart model from exercise 1 as follows assign a cost or benefit in dollar
based on your answer to the previous exercise adjust the misclassification costs for your cart model to reduce the
apply a cart model for predicting churn use default misclassification costs construct a table containing the following
1 construct a lift chart for the default cart model what is the estimated lift at 20 33 40 502 construct a single lift
1 what should one look for when evaluating a gains chart2 for model selection should model evaluation be performed on
1 when misclassification costs are involved what is the best model evaluation measure2 describe the trade-off between
1 what is the term used for the proportion of true positives in the medical literature why do we prefer to avoid this
1 in the case study explain why model 1 has better sensitivity lower proportion of false negatives and lower overall