When constructing the argument for a bagging tree strategy, the varImpPlot function displays feature importance graphically. For this we set the type argument to either equal 1 or 2. If type = 2, then what does this command? to show the average decrease in the predictive variable mean in a percentage form that R will use the average decrease in the Gini impurity index to compare the feature importance to show the feature importance as the average decrease in overall accuracy that R will use the average increase in the Gini impurity index to compare future importance Based on the Gini index, 0.10 implies a higher degree of purity because it is closer to 0 than 0.5. T or F Before constructing a decision tree, one of the first steps is identifying possible splits of the predictor variable.
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
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When constructing the argument for a bagging tree strategy, the varImpPlot
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to show the average decrease in the predictive variable mean in a percentage form
-
that R will use the average decrease in the Gini impurity index to compare the feature importance
-
to show the feature importance as the average decrease in overall accuracy
-
that R will use the average increase in the Gini impurity index to compare future importance
-
-
Based on the Gini index, 0.10 implies a higher degree of purity because it is closer to 0 than 0.5.
T or F
-
Before constructing a decision tree, one of the first steps is identifying possible splits of the predictor variable.
T or F
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